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Haghighat$, Blake Kaplan∗, Graydon Hoare∗, Boris Zbarsky∗, Jason Orendorff∗,\n\nJesse Ruderman∗, Edwin Smith#, Rick Reitmaier#, Michael Bebenita+, Mason Chang+#, Michael Franz+\n\nMozilla Corporation∗\n\n{gal,brendan,shaver,danderson,dmandelin,mrbkap,graydon,bz,jorendorff,jruderman}@mozilla.com\n\nAdobe Corporation#\n\n{edwsmith,rreitmai}@adobe.com\n\nIntel Corporation$\n\n{mohammad.r.haghighat}@intel.com\n\nUniversity of California, Irvine+\n\n{mbebenit,changm,franz}@uci.edu\n\nAbstract\nDynamic languages such as JavaScript are more difficult to com-\npile than statically typed ones. Since no concrete type information\nis available, traditional compilers need to emit generic code that can\nhandle all possible type combinations at runtime. We present an al-\nternative compilation technique for dynamically-typed languages\nthat identifies frequently executed loop traces at run-time and then\ngenerates machine code on the fly that is specialized for the ac-\ntual dynamic types occurring on each path through the loop. Our\nmethod provides cheap inter-procedural type specialization, and an\nelegant and efficient way of incrementally compiling lazily discov-\nered alternative paths through nested loops. We have implemented\na dynamic compiler for JavaScript based on our technique and we\nhave measured speedups of 10x and more for certain benchmark\nprograms.\n\nCategories and Subject Descriptors D.3.4 [Programming Lan-\nguages]: Processors — Incremental compilers, code generation.\n\nGeneral Terms Design, Experimentation, Measurement, Perfor-\nmance.\n\nKeywords JavaScript, just-in-time compilation, trace trees.\n\n1. Introduction\nDynamic languages such as JavaScript, Python, and Ruby, are pop-\nular since they are expressive, accessible to non-experts, and make\ndeployment as easy as distributing a source file. They are used for\nsmall scripts as well as for complex applications. JavaScript, for\nexample, is the de facto standard for client-side web programming\n\nPermission to make digital or hard copies of all or part of this work for personal or\nclassroom use is granted without fee provided that copies are not made or distributed\nfor profit or commercial advantage and that copies bear this notice and the full citation\non the first page. To copy otherwise, to republish, to post on servers or to redistribute\nto lists, requires prior specific permission and/or a fee.\nPLDI’09, June 15–20, 2009, Dublin, Ireland.\nCopyright c© 2009 ACM 978-1-60558-392-1/09/06. . . $5.00\n\nand is used for the application logic of browser-based productivity\napplications such as Google Mail, Google Docs and Zimbra Col-\nlaboration Suite. In this domain, in order to provide a fluid user\nexperience and enable a new generation of applications, virtual ma-\nchines must provide a low startup time and high performance.\n\nCompilers for statically typed languages rely on type informa-\ntion to generate efficient machine code. In a dynamically typed pro-\ngramming language such as JavaScript, the types of expressions\nmay vary at runtime. This means that the compiler can no longer\neasily transform operations into machine instructions that operate\non one specific type. Without exact type information, the compiler\nmust emit slower generalized machine code that can deal with all\npotential type combinations. While compile-time static type infer-\nence might be able to gather type information to generate opti-\nmized machine code, traditional static analysis is very expensive\nand hence not well suited for the highly interactive environment of\na web browser.\n\nWe present a trace-based compilation technique for dynamic\nlanguages that reconciles speed of compilation with excellent per-\nformance of the generated machine code. Our system uses a mixed-\nmode execution approach: the system starts running JavaScript in a\nfast-starting bytecode interpreter. As the program runs, the system\nidentifies hot (frequently executed) bytecode sequences, records\nthem, and compiles them to fast native code. We call such a se-\nquence of instructions a trace.\n\nUnlike method-based dynamic compilers, our dynamic com-\npiler operates at the granularity of individual loops. This design\nchoice is based on the expectation that programs spend most of\ntheir time in hot loops. Even in dynamically typed languages, we\nexpect hot loops to be mostly type-stable, meaning that the types of\nvalues are invariant. (12) For example, we would expect loop coun-\nters that start as integers to remain integers for all iterations. When\nboth of these expectations hold, a trace-based compiler can cover\nthe program execution with a small number of type-specialized, ef-\nficiently compiled traces.\n\nEach compiled trace covers one path through the program with\none mapping of values to types. When the VM executes a compiled\ntrace, it cannot guarantee that the same path will be followed\nor that the same types will occur in subsequent loop iterations.\n\n\n\nHence, recording and compiling a trace speculates that the path and\ntyping will be exactly as they were during recording for subsequent\niterations of the loop.\n\nEvery compiled trace contains all the guards (checks) required\nto validate the speculation. If one of the guards fails (if control\nflow is different, or a value of a different type is generated), the\ntrace exits. If an exit becomes hot, the VM can record a branch\ntrace starting at the exit to cover the new path. In this way, the VM\nrecords a trace tree covering all the hot paths through the loop.\n\nNested loops can be difficult to optimize for tracing VMs. In\na naı̈ve implementation, inner loops would become hot first, and\nthe VM would start tracing there. When the inner loop exits, the\nVM would detect that a different branch was taken. The VM would\ntry to record a branch trace, and find that the trace reaches not the\ninner loop header, but the outer loop header. At this point, the VM\ncould continue tracing until it reaches the inner loop header again,\nthus tracing the outer loop inside a trace tree for the inner loop.\nBut this requires tracing a copy of the outer loop for every side exit\nand type combination in the inner loop. In essence, this is a form\nof unintended tail duplication, which can easily overflow the code\ncache. Alternatively, the VM could simply stop tracing, and give up\non ever tracing outer loops.\n\nWe solve the nested loop problem by recording nested trace\ntrees. Our system traces the inner loop exactly as the naı̈ve version.\nThe system stops extending the inner tree when it reaches an outer\nloop, but then it starts a new trace at the outer loop header. When\nthe outer loop reaches the inner loop header, the system tries to call\nthe trace tree for the inner loop. If the call succeeds, the VM records\nthe call to the inner tree as part of the outer trace and finishes\nthe outer trace as normal. In this way, our system can trace any\nnumber of loops nested to any depth without causing excessive tail\nduplication.\n\nThese techniques allow a VM to dynamically translate a pro-\ngram to nested, type-specialized trace trees. Because traces can\ncross function call boundaries, our techniques also achieve the ef-\nfects of inlining. Because traces have no internal control-flow joins,\nthey can be optimized in linear time by a simple compiler (10).\nThus, our tracing VM efficiently performs the same kind of op-\ntimizations that would require interprocedural analysis in a static\noptimization setting. This makes tracing an attractive and effective\ntool to type specialize even complex function call-rich code.\n\nWe implemented these techniques for an existing JavaScript in-\nterpreter, SpiderMonkey. We call the resulting tracing VM Trace-\nMonkey. TraceMonkey supports all the JavaScript features of Spi-\nderMonkey, with a 2x-20x speedup for traceable programs.\n\nThis paper makes the following contributions:\n\n• We explain an algorithm for dynamically forming trace trees to\ncover a program, representing nested loops as nested trace trees.\n\n• We explain how to speculatively generate efficient type-specialized\ncode for traces from dynamic language programs.\n\n• We validate our tracing techniques in an implementation based\non the SpiderMonkey JavaScript interpreter, achieving 2x-20x\nspeedups on many programs.\n\nThe remainder of this paper is organized as follows. Section 3 is\na general overview of trace tree based compilation we use to cap-\nture and compile frequently executed code regions. In Section 4\nwe describe our approach of covering nested loops using a num-\nber of individual trace trees. In Section 5 we describe our trace-\ncompilation based speculative type specialization approach we use\nto generate efficient machine code from recorded bytecode traces.\nOur implementation of a dynamic type-specializing compiler for\nJavaScript is described in Section 6. Related work is discussed in\nSection 8. In Section 7 we evaluate our dynamic compiler based on\n\n1 for (var i = 2; i < 100; ++i) {\n2 if (!primes[i])\n3 continue;\n4 for (var k = i + i; i < 100; k += i)\n5 primes[k] = false;\n6 }\n\nFigure 1. Sample program: sieve of Eratosthenes. primes is\ninitialized to an array of 100 false values on entry to this code\nsnippet.\n\nInterpret \nBytecodes\n\nMonitor \n\nRecord\nLIR Trace\n\nExecute \nCompiled Trace\n\nEnter \nCompiled Trace\n\nCompile\nLIR Trace\n\nLeave \nCompiled Trace\n\nloop \nedge\n\nhot\nloop/exit\n\nabort \nrecording\n\nfinish at \nloop header\n\ncold/blacklisted\nloop/exit\n\ncompiled trace \nready\n\nloop edge with \nsame types\n\nside exit to \nexisting trace\n\nside exit,\nno existing trace\n\nOverhead \n\nInterpreting\n\nNative\n\nSymbol Key\n\nFigure 2. State machine describing the major activities of Trace-\nMonkey and the conditions that cause transitions to a new activ-\nity. In the dark box, TM executes JS as compiled traces. In the\nlight gray boxes, TM executes JS in the standard interpreter. White\nboxes are overhead. Thus, to maximize performance, we need to\nmaximize time spent in the darkest box and minimize time spent in\nthe white boxes. The best case is a loop where the types at the loop\nedge are the same as the types on entry–then TM can stay in native\ncode until the loop is done.\n\na set of industry benchmarks. The paper ends with conclusions in\nSection 9 and an outlook on future work is presented in Section 10.\n\n2. Overview: Example Tracing Run\nThis section provides an overview of our system by describing\nhow TraceMonkey executes an example program. The example\nprogram, shown in Figure 1, computes the first 100 prime numbers\nwith nested loops. The narrative should be read along with Figure 2,\nwhich describes the activities TraceMonkey performs and when it\ntransitions between the loops.\n\nTraceMonkey always begins executing a program in the byte-\ncode interpreter. Every loop back edge is a potential trace point.\nWhen the interpreter crosses a loop edge, TraceMonkey invokes\nthe trace monitor, which may decide to record or execute a native\ntrace. At the start of execution, there are no compiled traces yet, so\nthe trace monitor counts the number of times each loop back edge is\nexecuted until a loop becomes hot, currently after 2 crossings. Note\nthat the way our loops are compiled, the loop edge is crossed before\nentering the loop, so the second crossing occurs immediately after\nthe first iteration.\n\nHere is the sequence of events broken down by outer loop\niteration:\n\n\n\nv0 := ld state[748] // load primes from the trace activation record\nst sp[0], v0 // store primes to interpreter stack\n\nv1 := ld state[764] // load k from the trace activation record\nv2 := i2f(v1) // convert k from int to double\n\nst sp[8], v1 // store k to interpreter stack\nst sp[16], 0 // store false to interpreter stack\n\nv3 := ld v0[4] // load class word for primes\nv4 := and v3, -4 // mask out object class tag for primes\nv5 := eq v4, Array // test whether primes is an array\n\nxf v5 // side exit if v5 is false\nv6 := js_Array_set(v0, v2, false) // call function to set array element\nv7 := eq v6, 0 // test return value from call\n\nxt v7 // side exit if js_Array_set returns false.\n\nFigure 3. LIR snippet for sample program. This is the LIR recorded for line 5 of the sample program in Figure 1. The LIR encodes\nthe semantics in SSA form using temporary variables. The LIR also encodes all the stores that the interpreter would do to its data stack.\nSometimes these stores can be optimized away as the stack locations are live only on exits to the interpreter. Finally, the LIR records guards\nand side exits to verify the assumptions made in this recording: that primes is an array and that the call to set its element succeeds.\n\nmov edx, ebx(748) // load primes from the trace activation record\nmov edi(0), edx // (*) store primes to interpreter stack\nmov esi, ebx(764) // load k from the trace activation record\nmov edi(8), esi // (*) store k to interpreter stack\nmov edi(16), 0 // (*) store false to interpreter stack\nmov eax, edx(4) // (*) load object class word for primes\nand eax, -4 // (*) mask out object class tag for primes\ncmp eax, Array // (*) test whether primes is an array\njne side_exit_1 // (*) side exit if primes is not an array\nsub esp, 8 // bump stack for call alignment convention\npush false // push last argument for call\npush esi // push first argument for call\ncall js_Array_set // call function to set array element\nadd esp, 8 // clean up extra stack space\nmov ecx, ebx // (*) created by register allocator\ntest eax, eax // (*) test return value of js_Array_set\nje side_exit_2 // (*) side exit if call failed\n...\nside_exit_1:\nmov ecx, ebp(-4) // restore ecx\nmov esp, ebp // restore esp\njmp epilog // jump to ret statement\n\nFigure 4. x86 snippet for sample program. This is the x86 code compiled from the LIR snippet in Figure 3. Most LIR instructions compile\nto a single x86 instruction. Instructions marked with (*) would be omitted by an idealized compiler that knew that none of the side exits\nwould ever be taken. The 17 instructions generated by the compiler compare favorably with the 100+ instructions that the interpreter would\nexecute for the same code snippet, including 4 indirect jumps.\n\ni=2. This is the first iteration of the outer loop. The loop on\nlines 4-5 becomes hot on its second iteration, so TraceMonkey en-\nters recording mode on line 4. In recording mode, TraceMonkey\nrecords the code along the trace in a low-level compiler intermedi-\nate representation we call LIR. The LIR trace encodes all the oper-\nations performed and the types of all operands. The LIR trace also\nencodes guards, which are checks that verify that the control flow\nand types are identical to those observed during trace recording.\nThus, on later executions, if and only if all guards are passed, the\ntrace has the required program semantics.\n\nTraceMonkey stops recording when execution returns to the\nloop header or exits the loop. In this case, execution returns to the\nloop header on line 4.\n\nAfter recording is finished, TraceMonkey compiles the trace to\nnative code using the recorded type information for optimization.\nThe result is a native code fragment that can be entered if the\n\ninterpreter PC and the types of values match those observed when\ntrace recording was started. The first trace in our example, T45,\ncovers lines 4 and 5. This trace can be entered if the PC is at line 4,\ni and k are integers, and primes is an object. After compiling T45,\nTraceMonkey returns to the interpreter and loops back to line 1.\n\ni=3. Now the loop header at line 1 has become hot, so Trace-\nMonkey starts recording. When recording reaches line 4, Trace-\nMonkey observes that it has reached an inner loop header that al-\nready has a compiled trace, so TraceMonkey attempts to nest the\ninner loop inside the current trace. The first step is to call the inner\ntrace as a subroutine. This executes the loop on line 4 to completion\nand then returns to the recorder. TraceMonkey verifies that the call\nwas successful and then records the call to the inner trace as part of\nthe current trace. Recording continues until execution reaches line\n1, and at which point TraceMonkey finishes and compiles a trace\nfor the outer loop, T16.\n\n\n\ni=4. On this iteration, TraceMonkey calls T16. Because i=4, the\nif statement on line 2 is taken. This branch was not taken in the\noriginal trace, so this causes T16 to fail a guard and take a side exit.\nThe exit is not yet hot, so TraceMonkey returns to the interpreter,\nwhich executes the continue statement.\n\ni=5. TraceMonkey calls T16, which in turn calls the nested trace\nT45. T16 loops back to its own header, starting the next iteration\nwithout ever returning to the monitor.\n\ni=6. On this iteration, the side exit on line 2 is taken again. This\ntime, the side exit becomes hot, so a trace T23,1 is recorded that\ncovers line 3 and returns to the loop header. Thus, the end of T23,1\n\njumps directly to the start of T16. The side exit is patched so that\non future iterations, it jumps directly to T23,1.\n\nAt this point, TraceMonkey has compiled enough traces to cover\nthe entire nested loop structure, so the rest of the program runs\nentirely as native code.\n\n3. Trace Trees\nIn this section, we describe traces, trace trees, and how they are\nformed at run time. Although our techniques apply to any dynamic\nlanguage interpreter, we will describe them assuming a bytecode\ninterpreter to keep the exposition simple.\n\n3.1 Traces\nA trace is simply a program path, which may cross function call\nboundaries. TraceMonkey focuses on loop traces, that originate at\na loop edge and represent a single iteration through the associated\nloop.\n\nSimilar to an extended basic block, a trace is only entered at\nthe top, but may have many exits. In contrast to an extended basic\nblock, a trace can contain join nodes. Since a trace always only\nfollows one single path through the original program, however, join\nnodes are not recognizable as such in a trace and have a single\npredecessor node like regular nodes.\n\nA typed trace is a trace annotated with a type for every variable\n(including temporaries) on the trace. A typed trace also has an entry\ntype map giving the required types for variables used on the trace\nbefore they are defined. For example, a trace could have a type map\n(x: int, b: boolean), meaning that the trace may be entered\nonly if the value of the variable x is of type int and the value of b\nis of type boolean. The entry type map is much like the signature\nof a function.\n\nIn this paper, we only discuss typed loop traces, and we will\nrefer to them simply as “traces”. The key property of typed loop\ntraces is that they can be compiled to efficient machine code using\nthe same techniques used for typed languages.\n\nIn TraceMonkey, traces are recorded in trace-flavored SSA LIR\n(low-level intermediate representation). In trace-flavored SSA (or\nTSSA), phi nodes appear only at the entry point, which is reached\nboth on entry and via loop edges. The important LIR primitives\nare constant values, memory loads and stores (by address and\noffset), integer operators, floating-point operators, function calls,\nand conditional exits. Type conversions, such as integer to double,\nare represented by function calls. This makes the LIR used by\nTraceMonkey independent of the concrete type system and type\nconversion rules of the source language. The LIR operations are\ngeneric enough that the backend compiler is language independent.\nFigure 3 shows an example LIR trace.\n\nBytecode interpreters typically represent values in a various\ncomplex data structures (e.g., hash tables) in a boxed format (i.e.,\nwith attached type tag bits). Since a trace is intended to represent\nefficient code that eliminates all that complexity, our traces oper-\nate on unboxed values in simple variables and arrays as much as\npossible.\n\nA trace records all its intermediate values in a small activation\nrecord area. To make variable accesses fast on trace, the trace also\nimports local and global variables by unboxing them and copying\nthem to its activation record. Thus, the trace can read and write\nthese variables with simple loads and stores from a native activation\nrecording, independently of the boxing mechanism used by the\ninterpreter. When the trace exits, the VM boxes the values from\nthis native storage location and copies them back to the interpreter\nstructures.\n\nFor every control-flow branch in the source program, the\nrecorder generates conditional exit LIR instructions. These instruc-\ntions exit from the trace if required control flow is different from\nwhat it was at trace recording, ensuring that the trace instructions\nare run only if they are supposed to. We call these instructions\nguard instructions.\n\nMost of our traces represent loops and end with the special loop\nLIR instruction. This is just an unconditional branch to the top of\nthe trace. Such traces return only via guards.\n\nNow, we describe the key optimizations that are performed as\npart of recording LIR. All of these optimizations reduce complex\ndynamic language constructs to simple typed constructs by spe-\ncializing for the current trace. Each optimization requires guard in-\nstructions to verify their assumptions about the state and exit the\ntrace if necessary.\n\nType specialization.\nAll LIR primitives apply to operands of specific types. Thus,\n\nLIR traces are necessarily type-specialized, and a compiler can\neasily produce a translation that requires no type dispatches. A\ntypical bytecode interpreter carries tag bits along with each value,\nand to perform any operation, must check the tag bits, dynamically\ndispatch, mask out the tag bits to recover the untagged value,\nperform the operation, and then reapply tags. LIR omits everything\nexcept the operation itself.\n\nA potential problem is that some operations can produce values\nof unpredictable types. For example, reading a property from an\nobject could yield a value of any type, not necessarily the type\nobserved during recording. The recorder emits guard instructions\nthat conditionally exit if the operation yields a value of a different\ntype from that seen during recording. These guard instructions\nguarantee that as long as execution is on trace, the types of values\nmatch those of the typed trace. When the VM observes a side exit\nalong such a type guard, a new typed trace is recorded originating\nat the side exit location, capturing the new type of the operation in\nquestion.\n\nRepresentation specialization: objects. In JavaScript, name\nlookup semantics are complex and potentially expensive because\nthey include features like object inheritance and eval. To evaluate\nan object property read expression like o.x, the interpreter must\nsearch the property map of o and all of its prototypes and parents.\nProperty maps can be implemented with different data structures\n(e.g., per-object hash tables or shared hash tables), so the search\nprocess also must dispatch on the representation of each object\nfound during search. TraceMonkey can simply observe the result of\nthe search process and record the simplest possible LIR to access\nthe property value. For example, the search might finds the value of\no.x in the prototype of o, which uses a shared hash-table represen-\ntation that places x in slot 2 of a property vector. Then the recorded\ncan generate LIR that reads o.x with just two or three loads: one to\nget the prototype, possibly one to get the property value vector, and\none more to get slot 2 from the vector. This is a vast simplification\nand speedup compared to the original interpreter code. Inheritance\nrelationships and object representations can change during execu-\ntion, so the simplified code requires guard instructions that ensure\nthe object representation is the same. In TraceMonkey, objects’ rep-\n\n\n\nresentations are assigned an integer key called the object shape.\nThus, the guard is a simple equality check on the object shape.\n\nRepresentation specialization: numbers. JavaScript has no\ninteger type, only a Number type that is the set of 64-bit IEEE-\n754 floating-pointer numbers (“doubles”). But many JavaScript\noperators, in particular array accesses and bitwise operators, really\noperate on integers, so they first convert the number to an integer,\nand then convert any integer result back to a double.1 Clearly, a\nJavaScript VM that wants to be fast must find a way to operate on\nintegers directly and avoid these conversions.\n\nIn TraceMonkey, we support two representations for numbers:\nintegers and doubles. The interpreter uses integer representations\nas much as it can, switching for results that can only be represented\nas doubles. When a trace is started, some values may be imported\nand represented as integers. Some operations on integers require\nguards. For example, adding two integers can produce a value too\nlarge for the integer representation.\n\nFunction inlining. LIR traces can cross function boundaries\nin either direction, achieving function inlining. Move instructions\nneed to be recorded for function entry and exit to copy arguments\nin and return values out. These move statements are then optimized\naway by the compiler using copy propagation. In order to be able\nto return to the interpreter, the trace must also generate LIR to\nrecord that a call frame has been entered and exited. The frame\nentry and exit LIR saves just enough information to allow the\nintepreter call stack to be restored later and is much simpler than\nthe interpreter’s standard call code. If the function being entered\nis not constant (which in JavaScript includes any call by function\nname), the recorder must also emit LIR to guard that the function\nis the same.\n\nGuards and side exits. Each optimization described above\nrequires one or more guards to verify the assumptions made in\ndoing the optimization. A guard is just a group of LIR instructions\nthat performs a test and conditional exit. The exit branches to a\nside exit, a small off-trace piece of LIR that returns a pointer to\na structure that describes the reason for the exit along with the\ninterpreter PC at the exit point and any other data needed to restore\nthe interpreter’s state structures.\n\nAborts. Some constructs are difficult to record in LIR traces.\nFor example, eval or calls to external functions can change the\nprogram state in unpredictable ways, making it difficult for the\ntracer to know the current type map in order to continue tracing.\nA tracing implementation can also have any number of other limi-\ntations, e.g.,a small-memory device may limit the length of traces.\nWhen any situation occurs that prevents the implementation from\ncontinuing trace recording, the implementation aborts trace record-\ning and returns to the trace monitor.\n\n3.2 Trace Trees\nEspecially simple loops, namely those where control flow, value\ntypes, value representations, and inlined functions are all invariant,\ncan be represented by a single trace. But most loops have at least\nsome variation, and so the program will take side exits from the\nmain trace. When a side exit becomes hot, TraceMonkey starts a\nnew branch trace from that point and patches the side exit to jump\ndirectly to that trace. In this way, a single trace expands on demand\nto a single-entry, multiple-exit trace tree.\n\nThis section explains how trace trees are formed during execu-\ntion. The goal is to form trace trees during execution that cover all\nthe hot paths of the program.\n\n1 Arrays are actually worse than this: if the index value is a number, it must\nbe converted from a double to a string for the property access operator, and\nthen to an integer internally to the array implementation.\n\nStarting a tree. Tree trees always start at loop headers, because\nthey are a natural place to look for hot paths. In TraceMonkey, loop\nheaders are easy to detect–the bytecode compiler ensures that a\nbytecode is a loop header iff it is the target of a backward branch.\nTraceMonkey starts a tree when a given loop header has been exe-\ncuted a certain number of times (2 in the current implementation).\nStarting a tree just means starting recording a trace for the current\npoint and type map and marking the trace as the root of a tree. Each\ntree is associated with a loop header and type map, so there may be\nseveral trees for a given loop header.\n\nClosing the loop. Trace recording can end in several ways.\nIdeally, the trace reaches the loop header where it started with\n\nthe same type map as on entry. This is called a type-stable loop\niteration. In this case, the end of the trace can jump right to the\nbeginning, as all the value representations are exactly as needed to\nenter the trace. The jump can even skip the usual code that would\ncopy out the state at the end of the trace and copy it back in to the\ntrace activation record to enter a trace.\n\nIn certain cases the trace might reach the loop header with a\ndifferent type map. This scenario is sometime observed for the first\niteration of a loop. Some variables inside the loop might initially be\nundefined, before they are set to a concrete type during the first loop\niteration. When recording such an iteration, the recorder cannot\nlink the trace back to its own loop header since it is type-unstable.\nInstead, the iteration is terminated with a side exit that will always\nfail and return to the interpreter. At the same time a new trace is\nrecorded with the new type map. Every time an additional type-\nunstable trace is added to a region, its exit type map is compared to\nthe entry map of all existing traces in case they complement each\nother. With this approach we are able to cover type-unstable loop\niterations as long they eventually form a stable equilibrium.\n\nFinally, the trace might exit the loop before reaching the loop\nheader, for example because execution reaches a break or return\nstatement. In this case, the VM simply ends the trace with an exit\nto the trace monitor.\n\nAs mentioned previously, we may speculatively chose to rep-\nresent certain Number-typed values as integers on trace. We do so\nwhen we observe that Number-typed variables contain an integer\nvalue at trace entry. If during trace recording the variable is unex-\npectedly assigned a non-integer value, we have to widen the type\nof the variable to a double. As a result, the recorded trace becomes\ninherently type-unstable since it starts with an integer value but\nends with a double value. This represents a mis-speculation, since\nat trace entry we specialized the Number-typed value to an integer,\nassuming that at the loop edge we would again find an integer value\nin the variable, allowing us to close the loop. To avoid future spec-\nulative failures involving this variable, and to obtain a type-stable\ntrace we note the fact that the variable in question as been observed\nto sometimes hold non-integer values in an advisory data structure\nwhich we call the “oracle”.\n\nWhen compiling loops, we consult the oracle before specializ-\ning values to integers. Speculation towards integers is performed\nonly if no adverse information is known to the oracle about that\nparticular variable. Whenever we accidentally compile a loop that\nis type-unstable due to mis-speculation of a Number-typed vari-\nable, we immediately trigger the recording of a new trace, which\nbased on the now updated oracle information will start with a dou-\nble value and thus become type stable.\n\nExtending a tree. Side exits lead to different paths through\nthe loop, or paths with different types or representations. Thus, to\ncompletely cover the loop, the VM must record traces starting at all\nside exits. These traces are recorded much like root traces: there is\na counter for each side exit, and when the counter reaches a hotness\nthreshold, recording starts. Recording stops exactly as for the root\ntrace, using the loop header of the root trace as the target to reach.\n\n\n\nOur implementation does not extend at all side exits. It extends\nonly if the side exit is for a control-flow branch, and only if the side\nexit does not leave the loop. In particular we do not want to extend\na trace tree along a path that leads to an outer loop, because we\nwant to cover such paths in an outer tree through tree nesting.\n\n3.3 Blacklisting\nSometimes, a program follows a path that cannot be compiled\ninto a trace, usually because of limitations in the implementation.\nTraceMonkey does not currently support recording throwing and\ncatching of arbitrary exceptions. This design trade off was chosen,\nbecause exceptions are usually rare in JavaScript. However, if a\nprogram opts to use exceptions intensively, we would suddenly\nincur a punishing runtime overhead if we repeatedly try to record\na trace for this path and repeatedly fail to do so, since we abort\ntracing every time we observe an exception being thrown.\n\nAs a result, if a hot loop contains traces that always fail, the VM\ncould potentially run much more slowly than the base interpreter:\nthe VM repeatedly spends time trying to record traces, but is never\nable to run any. To avoid this problem, whenever the VM is about\nto start tracing, it must try to predict whether it will finish the trace.\n\nOur prediction algorithm is based on blacklisting traces that\nhave been tried and failed. When the VM fails to finish a trace start-\ning at a given point, the VM records that a failure has occurred. The\nVM also sets a counter so that it will not try to record a trace starting\nat that point until it is passed a few more times (32 in our imple-\nmentation). This backoff counter gives temporary conditions that\nprevent tracing a chance to end. For example, a loop may behave\ndifferently during startup than during its steady-state execution. Af-\nter a given number of failures (2 in our implementation), the VM\nmarks the fragment as blacklisted, which means the VM will never\nagain start recording at that point.\n\nAfter implementing this basic strategy, we observed that for\nsmall loops that get blacklisted, the system can spend a noticeable\namount of time just finding the loop fragment and determining that\nit has been blacklisted. We now avoid that problem by patching the\nbytecode. We define an extra no-op bytecode that indicates a loop\nheader. The VM calls into the trace monitor every time the inter-\npreter executes a loop header no-op. To blacklist a fragment, we\nsimply replace the loop header no-op with a regular no-op. Thus,\nthe interpreter will never again even call into the trace monitor.\n\nThere is a related problem we have not yet solved, which occurs\nwhen a loop meets all of these conditions:\n\n• The VM can form at least one root trace for the loop.\n• There is at least one hot side exit for which the VM cannot\n\ncomplete a trace.\n• The loop body is short.\n\nIn this case, the VM will repeatedly pass the loop header, search\nfor a trace, find it, execute it, and fall back to the interpreter.\nWith a short loop body, the overhead of finding and calling the\ntrace is high, and causes performance to be even slower than the\nbasic interpreter. So far, in this situation we have improved the\nimplementation so that the VM can complete the branch trace.\nBut it is hard to guarantee that this situation will never happen.\nAs future work, this situation could be avoided by detecting and\nblacklisting loops for which the average trace call executes few\nbytecodes before returning to the interpreter.\n\n4. Nested Trace Tree Formation\nFigure 7 shows basic trace tree compilation (11) applied to a nested\nloop where the inner loop contains two paths. Usually, the inner\nloop (with header at i2) becomes hot first, and a trace tree is rooted\nat that point. For example, the first recorded trace may be a cycle\n\nT\n\nTrunk
Trace\n\nTree
Anchor\n\nTrace
Anchor\n\nBranch
Trace\n\nGuard\n\nSide
Exit\n\nFigure 5. A tree with two traces, a trunk trace and one branch\ntrace. The trunk trace contains a guard to which a branch trace was\nattached. The branch trace contain a guard that may fail and trigger\na side exit. Both the trunk and the branch trace loop back to the tree\nanchor, which is the beginning of the trace tree.\n\nTrace
2Trace
1 Trace
2Trace
1\n\nClosed Linked Linked Linked\n\nNumber\n\nNumber\n\nString\n\nString String\n\nString\n\nBoolean\n\nTrace
2Trace
1 Trace
3\n\nLinked\nLinked Linked Closed\n\nNumber\n\nNumber Number\n\nBoolean Number\n\nBoolean Number\n\nBoolean\n\n(a) (b)\n\n(c)\n\nFigure 6. We handle type-unstable loops by allowing traces to\ncompile that cannot loop back to themselves due to a type mis-\nmatch. As such traces accumulate, we attempt to connect their loop\nedges to form groups of trace trees that can execute without having\nto side-exit to the interpreter to cover odd type cases. This is par-\nticularly important for nested trace trees where an outer tree tries to\ncall an inner tree (or in this case a forest of inner trees), since inner\nloops frequently have initially undefined values which change type\nto a concrete value after the first iteration.\n\nthrough the inner loop, {i2, i3, i5, α}. The α symbol is used to\nindicate that the trace loops back the tree anchor.\n\nWhen execution leaves the inner loop, the basic design has two\nchoices. First, the system can stop tracing and give up on compiling\nthe outer loop, clearly an undesirable solution. The other choice is\nto continue tracing, compiling traces for the outer loop inside the\ninner loop’s trace tree.\n\nFor example, the program might exit at i5 and record a branch\ntrace that incorporates the outer loop: {i5, i7, i1, i6, i7, i1, α}.\nLater, the program might take the other branch at i2 and then\nexit, recording another branch trace incorporating the outer loop:\n{i2, i4, i5, i7, i1, i6, i7, i1, α}. Thus, the outer loop is recorded and\ncompiled twice, and both copies must be retained in the trace cache.\n\n\n\ni2\n\ni3 i4\n\ni5\n\ni1\n\ni6\n\ni7\n\nt1\n\nt2\n\nTree
Call\n\nOuter
Tree\n\nNested
Tree\n\nExit
Guard\n\n(a) (b)\n\nFigure 7. Control flow graph of a nested loop with an if statement\ninside the inner most loop (a). An inner tree captures the inner\nloop, and is nested inside an outer tree which “calls” the inner tree.\nThe inner tree returns to the outer tree once it exits along its loop\ncondition guard (b).\n\nIn general, if loops are nested to depth k, and each loop has n paths\n(on geometric average), this naı̈ve strategy yields O(nk) traces,\nwhich can easily fill the trace cache.\n\nIn order to execute programs with nested loops efficiently, a\ntracing system needs a technique for covering the nested loops with\nnative code without exponential trace duplication.\n\n4.1 Nesting Algorithm\nThe key insight is that if each loop is represented by its own trace\ntree, the code for each loop can be contained only in its own tree,\nand outer loop paths will not be duplicated. Another key fact is that\nwe are not tracing arbitrary bytecodes that might have irreduceable\ncontrol flow graphs, but rather bytecodes produced by a compiler\nfor a language with structured control flow. Thus, given two loop\nedges, the system can easily determine whether they are nested\nand which is the inner loop. Using this knowledge, the system can\ncompile inner and outer loops separately, and make the outer loop’s\ntraces call the inner loop’s trace tree.\n\nThe algorithm for building nested trace trees is as follows. We\nstart tracing at loop headers exactly as in the basic tracing system.\nWhen we exit a loop (detected by comparing the interpreter PC\nwith the range given by the loop edge), we stop the trace. The\nkey step of the algorithm occurs when we are recording a trace\nfor loop LR (R for loop being recorded) and we reach the header\nof a different loop LO (O for other loop). Note that LO must be an\ninner loop of LR because we stop the trace when we exit a loop.\n\n• If LO has a type-matching compiled trace tree, we call LO as\na nested trace tree. If the call succeeds, then we record the call\nin the trace for LR. On future executions, the trace for LR will\ncall the inner trace directly.\n\n• If LO does not have a type-matching compiled trace tree yet,\nwe have to obtain it before we are able to proceed. In order\nto do this, we simply abort recording the first trace. The trace\nmonitor will see the inner loop header, and will immediately\nstart recording the inner loop. 2\n\nIf all the loops in a nest are type-stable, then loop nesting creates\nno duplication. Otherwise, if loops are nested to a depth k, and each\n\n2 Instead of aborting the outer recording, we could principally merely sus-\npend the recording, but that would require the implementation to be able\nto record several traces simultaneously, complicating the implementation,\nwhile saving only a few iterations in the interpreter.\n\ni2\n\ni3\n\ni1\n\ni6\n\ni4\n\ni5\n\nt2\n\nt1\n\nt4\n\nExit
Guard\n\nNested
Tree\n\nFigure 8. Control flow graph of a loop with two nested loops (left)\nand its nested trace tree configuration (right). The outer tree calls\nthe two inner nested trace trees and places guards at their side exit\nlocations.\n\nloop is entered with m different type maps (on geometric average),\nthen we compile O(mk) copies of the innermost loop. As long as\nm is close to 1, the resulting trace trees will be tractable.\n\nAn important detail is that the call to the inner trace tree must act\nlike a function call site: it must return to the same point every time.\nThe goal of nesting is to make inner and outer loops independent;\nthus when the inner tree is called, it must exit to the same point\nin the outer tree every time with the same type map. Because we\ncannot actually guarantee this property, we must guard on it after\nthe call, and side exit if the property does not hold. A common\nreason for the inner tree not to return to the same point would\nbe if the inner tree took a new side exit for which it had never\ncompiled a trace. At this point, the interpreter PC is in the inner\ntree, so we cannot continue recording or executing the outer tree.\nIf this happens during recording, we abort the outer trace, to give\nthe inner tree a chance to finish growing. A future execution of the\nouter tree would then be able to properly finish and record a call to\nthe inner tree. If an inner tree side exit happens during execution of\na compiled trace for the outer tree, we simply exit the outer trace\nand start recording a new branch in the inner tree.\n\n4.2 Blacklisting with Nesting\nThe blacklisting algorithm needs modification to work well with\nnesting. The problem is that outer loop traces often abort during\nstartup (because the inner tree is not available or takes a side exit),\nwhich would lead to their being quickly blacklisted by the basic\nalgorithm.\n\nThe key observation is that when an outer trace aborts because\nthe inner tree is not ready, this is probably a temporary condition.\nThus, we should not count such aborts toward blacklisting as long\nas we are able to build up more traces for the inner tree.\n\nIn our implementation, when an outer tree aborts on the inner\ntree, we increment the outer tree’s blacklist counter as usual and\nback off on compiling it. When the inner tree finishes a trace, we\ndecrement the blacklist counter on the outer loop, “forgiving” the\nouter loop for aborting previously. We also undo the backoff so that\nthe outer tree can start immediately trying to compile the next time\nwe reach it.\n\n5. Trace Tree Optimization\nThis section explains how a recorded trace is translated to an\noptimized machine code trace. The trace compilation subsystem,\nNANOJIT, is separate from the VM and can be used for other\napplications.\n\n\n\n5.1 Optimizations\nBecause traces are in SSA form and have no join points or φ-\nnodes, certain optimizations are easy to implement. In order to\nget good startup performance, the optimizations must run quickly,\nso we chose a small set of optimizations. We implemented the\noptimizations as pipelined filters so that they can be turned on and\noff independently, and yet all run in just two loop passes over the\ntrace: one forward and one backward.\n\nEvery time the trace recorder emits a LIR instruction, the in-\nstruction is immediately passed to the first filter in the forward\npipeline. Thus, forward filter optimizations are performed as the\ntrace is recorded. Each filter may pass each instruction to the next\nfilter unchanged, write a different instruction to the next filter, or\nwrite no instruction at all. For example, the constant folding filter\ncan replace a multiply instruction like v13 := mul3, 1000 with a\nconstant instruction v13 = 3000.\n\nWe currently apply four forward filters:\n\n• On ISAs without floating-point instructions, a soft-float filter\nconverts floating-point LIR instructions to sequences of integer\ninstructions.\n\n• CSE (constant subexpression elimination),\n• expression simplification, including constant folding and a few\n\nalgebraic identities (e.g., a− a = 0), and\n• source language semantic-specific expression simplification,\n\nprimarily algebraic identities that allow DOUBLE to be replaced\nwith INT. For example, LIR that converts an INT to a DOUBLE\nand then back again would be removed by this filter.\n\nWhen trace recording is completed, nanojit runs the backward\noptimization filters. These are used for optimizations that require\nbackward program analysis. When running the backward filters,\nnanojit reads one LIR instruction at a time, and the reads are passed\nthrough the pipeline.\n\nWe currently apply three backward filters:\n\n• Dead data-stack store elimination. The LIR trace encodes many\nstores to locations in the interpreter stack. But these values are\nnever read back before exiting the trace (by the interpreter or\nanother trace). Thus, stores to the stack that are overwritten\nbefore the next exit are dead. Stores to locations that are off\nthe top of the interpreter stack at future exits are also dead.\n\n• Dead call-stack store elimination. This is the same optimization\nas above, except applied to the interpreter’s call stack used for\nfunction call inlining.\n\n• Dead code elimination. This eliminates any operation that\nstores to a value that is never used.\n\nAfter a LIR instruction is successfully read (“pulled”) from\nthe backward filter pipeline, nanojit’s code generator emits native\nmachine instruction(s) for it.\n\n5.2 Register Allocation\nWe use a simple greedy register allocator that makes a single\nbackward pass over the trace (it is integrated with the code gen-\nerator). By the time the allocator has reached an instruction like\nv3 = add v1, v2, it has already assigned a register to v3. If v1 and\nv2 have not yet been assigned registers, the allocator assigns a free\nregister to each. If there are no free registers, a value is selected for\nspilling. We use a class heuristic that selects the “oldest” register-\ncarried value (6).\n\nThe heuristic considers the set R of values v in registers imme-\ndiately after the current instruction for spilling. Let vm be the last\ninstruction before the current where each v is referred to. Then the\n\nTag JS Type Description\nxx1 number 31-bit integer representation\n000 object pointer to JSObject handle\n010 number pointer to double handle\n100 string pointer to JSString handle\n110 boolean enumeration for null, undefined, true, false\n\nnull, or\nundefined\n\nFigure 9. Tagged values in the SpiderMonkey JS interpreter.\nTesting tags, unboxing (extracting the untagged value) and boxing\n(creating tagged values) are significant costs. Avoiding these costs\nis a key benefit of tracing.\n\nheuristic selects v with minimum vm. The motivation is that this\nfrees up a register for as long as possible given a single spill.\n\nIf we need to spill a value vs at this point, we generate the\nrestore code just after the code for the current instruction. The\ncorresponding spill code is generated just after the last point where\nvs was used. The register that was assigned to vs is marked free for\nthe preceding code, because that register can now be used freely\nwithout affecting the following code\n\n6. Implementation\nTo demonstrate the effectiveness of our approach, we have im-\nplemented a trace-based dynamic compiler for the SpiderMonkey\nJavaScript Virtual Machine (4). SpiderMonkey is the JavaScript\nVM embedded in Mozilla’s Firefox open-source web browser (2),\nwhich is used by more than 200 million users world-wide. The core\nof SpiderMonkey is a bytecode interpreter implemented in C++.\n\nIn SpiderMonkey, all JavaScript values are represented by the\ntype jsval. A jsval is machine word in which up to the 3 of the\nleast significant bits are a type tag, and the remaining bits are data.\nSee Figure 6 for details. All pointers contained in jsvals point to\nGC-controlled blocks aligned on 8-byte boundaries.\n\nJavaScript object values are mappings of string-valued property\nnames to arbitrary values. They are represented in one of two ways\nin SpiderMonkey. Most objects are represented by a shared struc-\ntural description, called the object shape, that maps property names\nto array indexes using a hash table. The object stores a pointer to\nthe shape and the array of its own property values. Objects with\nlarge, unique sets of property names store their properties directly\nin a hash table.\n\nThe garbage collector is an exact, non-generational, stop-the-\nworld mark-and-sweep collector.\n\nIn the rest of this section we discuss key areas of the TraceMon-\nkey implementation.\n\n6.1 Calling Compiled Traces\nCompiled traces are stored in a trace cache, indexed by intepreter\nPC and type map. Traces are compiled so that they may be\ncalled as functions using standard native calling conventions (e.g.,\nFASTCALL on x86).\n\nThe interpreter must hit a loop edge and enter the monitor in\norder to call a native trace for the first time. The monitor computes\nthe current type map, checks the trace cache for a trace for the\ncurrent PC and type map, and if it finds one, executes the trace.\n\nTo execute a trace, the monitor must build a trace activation\nrecord containing imported local and global variables, temporary\nstack space, and space for arguments to native calls. The local and\nglobal values are then copied from the interpreter state to the trace\nactivation record. Then, the trace is called like a normal C function\npointer.\n\n\n\nWhen a trace call returns, the monitor restores the interpreter\nstate. First, the monitor checks the reason for the trace exit and\napplies blacklisting if needed. Then, it pops or synthesizes inter-\npreter JavaScript call stack frames as needed. Finally, it copies the\nimported variables back from the trace activation record to the in-\nterpreter state.\n\nAt least in the current implementation, these steps have a non-\nnegligible runtime cost, so minimizing the number of interpreter-\nto-trace and trace-to-interpreter transitions is essential for perfor-\nmance. (see also Section 3.3). Our experiments (see Figure 12)\nshow that for programs we can trace well such transitions hap-\npen infrequently and hence do not contribute significantly to total\nruntime. In a few programs, where the system is prevented from\nrecording branch traces for hot side exits by aborts, this cost can\nrise to up to 10% of total execution time.\n\n6.2 Trace Stitching\nTransitions from a trace to a branch trace at a side exit avoid the\ncosts of calling traces from the monitor, in a feature called trace\nstitching. At a side exit, the exiting trace only needs to write live\nregister-carried values back to its trace activation record. In our im-\nplementation, identical type maps yield identical activation record\nlayouts, so the trace activation record can be reused immediately\nby the branch trace.\n\nIn programs with branchy trace trees with small traces, trace\nstitching has a noticeable cost. Although writing to memory and\nthen soon reading back would be expected to have a high L1\ncache hit rate, for small traces the increased instruction count has\na noticeable cost. Also, if the writes and reads are very close\nin the dynamic instruction stream, we have found that current\nx86 processors often incur penalties of 6 cycles or more (e.g., if\nthe instructions use different base registers with equal values, the\nprocessor may not be able to detect that the addresses are the same\nright away).\n\nThe alternate solution is to recompile an entire trace tree, thus\nachieving inter-trace register allocation (10). The disadvantage is\nthat tree recompilation takes time quadratic in the number of traces.\nWe believe that the cost of recompiling a trace tree every time\na branch is added would be prohibitive. That problem might be\nmitigated by recompiling only at certain points, or only for very\nhot, stable trees.\n\nIn the future, multicore hardware is expected to be common,\nmaking background tree recompilation attractive. In a closely re-\nlated project (13) background recompilation yielded speedups of\nup to 1.25x on benchmarks with many branch traces. We plan to\napply this technique to TraceMonkey as future work.\n\n6.3 Trace Recording\nThe job of the trace recorder is to emit LIR with identical semantics\nto the currently running interpreter bytecode trace. A good imple-\nmentation should have low impact on non-tracing interpreter per-\nformance and a convenient way for implementers to maintain se-\nmantic equivalence.\n\nIn our implementation, the only direct modification to the inter-\npreter is a call to the trace monitor at loop edges. In our benchmark\nresults (see Figure 12) the total time spent in the monitor (for all\nactivities) is usually less than 5%, so we consider the interpreter\nimpact requirement met. Incrementing the loop hit counter is ex-\npensive because it requires us to look up the loop in the trace cache,\nbut we have tuned our loops to become hot and trace very quickly\n(on the second iteration). The hit counter implementation could be\nimproved, which might give us a small increase in overall perfor-\nmance, as well as more flexibility with tuning hotness thresholds.\nOnce a loop is blacklisted we never call into the trace monitor for\nthat loop (see Section 3.3).\n\nRecording is activated by a pointer swap that sets the inter-\npreter’s dispatch table to call a single “interrupt” routine for ev-\nery bytecode. The interrupt routine first calls a bytecode-specific\nrecording routine. Then, it turns off recording if necessary (e.g.,\nthe trace ended). Finally, it jumps to the standard interpreter byte-\ncode implementation. Some bytecodes have effects on the type map\nthat cannot be predicted before executing the bytecode (e.g., call-\ning String.charCodeAt, which returns an integer or NaN if the\nindex argument is out of range). For these, we arrange for the inter-\npreter to call into the recorder again after executing the bytecode.\nSince such hooks are relatively rare, we embed them directly into\nthe interpreter, with an additional runtime check to see whether a\nrecorder is currently active.\n\nWhile separating the interpreter from the recorder reduces indi-\nvidual code complexity, it also requires careful implementation and\nextensive testing to achieve semantic equivalence.\n\nIn some cases achieving this equivalence is difficult since Spi-\nderMonkey follows a fat-bytecode design, which was found to be\nbeneficial to pure interpreter performance.\n\nIn fat-bytecode designs, individual bytecodes can implement\ncomplex processing (e.g., the getprop bytecode, which imple-\nments full JavaScript property value access, including special cases\nfor cached and dense array access).\n\nFat bytecodes have two advantages: fewer bytecodes means\nlower dispatch cost, and bigger bytecode implementations give the\ncompiler more opportunities to optimize the interpreter.\n\nFat bytecodes are a problem for TraceMonkey because they\nrequire the recorder to reimplement the same special case logic\nin the same way. Also, the advantages are reduced because (a)\ndispatch costs are eliminated entirely in compiled traces, (b) the\ntraces contain only one special case, not the interpreter’s large\nchunk of code, and (c) TraceMonkey spends less time running the\nbase interpreter.\n\nOne way we have mitigated these problems is by implementing\ncertain complex bytecodes in the recorder as sequences of simple\nbytecodes. Expressing the original semantics this way is not too dif-\nficult, and recording simple bytecodes is much easier. This enables\nus to retain the advantages of fat bytecodes while avoiding some of\ntheir problems for trace recording. This is particularly effective for\nfat bytecodes that recurse back into the interpreter, for example to\nconvert an object into a primitive value by invoking a well-known\nmethod on the object, since it lets us inline this function call.\n\nIt is important to note that we split fat opcodes into thinner op-\ncodes only during recording. When running purely interpretatively\n(i.e. code that has been blacklisted), the interpreter directly and ef-\nficiently executes the fat opcodes.\n\n6.4 Preemption\nSpiderMonkey, like many VMs, needs to preempt the user program\nperiodically. The main reasons are to prevent infinitely looping\nscripts from locking up the host system and to schedule GC.\n\nIn the interpreter, this had been implemented by setting a “pre-\nempt now” flag that was checked on every backward jump. This\nstrategy carried over into TraceMonkey: the VM inserts a guard on\nthe preemption flag at every loop edge. We measured less than a\n1% increase in runtime on most benchmarks for this extra guard.\nIn practice, the cost is detectable only for programs with very short\nloops.\n\nWe tested and rejected a solution that avoided the guards by\ncompiling the loop edge as an unconditional jump, and patching\nthe jump target to an exit routine when preemption is required.\nThis solution can make the normal case slightly faster, but then\npreemption becomes very slow. The implementation was also very\ncomplex, especially trying to restart execution after the preemption.\n\n\n\n6.5 Calling External Functions\nLike most interpreters, SpiderMonkey has a foreign function inter-\nface (FFI) that allows it to call C builtins and host system functions\n(e.g., web browser control and DOM access). The FFI has a stan-\ndard signature for JS-callable functions, the key argument of which\nis an array of boxed values. External functions called through the\nFFI interact with the program state through an interpreter API (e.g.,\nto read a property from an argument). There are also certain inter-\npreter builtins that do not use the FFI, but interact with the program\nstate in the same way, such as the CallIteratorNext function\nused with iterator objects. TraceMonkey must support this FFI in\norder to speed up code that interacts with the host system inside hot\nloops.\n\nCalling external functions from TraceMonkey is potentially dif-\nficult because traces do not update the interpreter state until exit-\ning. In particular, external functions may need the call stack or the\nglobal variables, but they may be out of date.\n\nFor the out-of-date call stack problem, we refactored some of\nthe interpreter API implementation functions to re-materialize the\ninterpreter call stack on demand.\n\nWe developed a C++ static analysis and annotated some inter-\npreter functions in order to verify that the call stack is refreshed\nat any point it needs to be used. In order to access the call stack,\na function must be annotated as either FORCESSTACK or RE-\nQUIRESSTACK. These annotations are also required in order to call\nREQUIRESSTACK functions, which are presumed to access the call\nstack transitively. FORCESSTACK is a trusted annotation, applied\nto only 5 functions, that means the function refreshes the call stack.\nREQUIRESSTACK is an untrusted annotation that means the func-\ntion may only be called if the call stack has already been refreshed.\n\nSimilarly, we detect when host functions attempt to directly\nread or write global variables, and force the currently running trace\nto side exit. This is necessary since we cache and unbox global\nvariables into the activation record during trace execution.\n\nSince both call-stack access and global variable access are\nrarely performed by host functions, performance is not significantly\naffected by these safety mechanisms.\n\nAnother problem is that external functions can reenter the inter-\npreter by calling scripts, which in turn again might want to access\nthe call stack or global variables. To address this problem, we made\nthe VM set a flag whenever the interpreter is reentered while a com-\npiled trace is running.\n\nEvery call to an external function then checks this flag and exits\nthe trace immediately after returning from the external function call\nif it is set. There are many external functions that seldom or never\nreenter, and they can be called without problem, and will cause\ntrace exit only if necessary.\n\nThe FFI’s boxed value array requirement has a performance\ncost, so we defined a new FFI that allows C functions to be an-\nnotated with their argument types so that the tracer can call them\ndirectly, without unnecessary argument conversions.\n\nCurrently, we do not support calling native property get and set\noverride functions or DOM functions directly from trace. Support\nis planned future work.\n\n6.6 Correctness\nDuring development, we had access to existing JavaScript test\nsuites, but most of them were not designed with tracing VMs in\nmind and contained few loops.\n\nOne tool that helped us greatly was Mozilla’s JavaScript fuzz\ntester, JSFUNFUZZ, which generates random JavaScript programs\nby nesting random language elements. We modified JSFUNFUZZ\nto generate loops, and also to test more heavily certain constructs\nwe suspected would reveal flaws in our implementation. For exam-\nple, we suspected bugs in TraceMonkey’s handling of type-unstable\n\n!\"# $!\"# %!\"# &!\"# '!\"# (!\"# )!\"# *!\"# +!\"# ,!\"# $!!\"#\n\n&-./012#3%4%56#\n\n&-.789:;#3%4,56#\n\n&-.9<=>9</2#3$4%56#\n\n<//2??.1@A<9=.>922?#3!4,56#\n\n<//2??.B<AAC0/;#3%4%56#\n\n<//2??.A18-=#3'4%56#\n\n<//2??.A?@2D2#3&4!56#\n\n1@>8:?.&1@>.1@>?.@A.1=>2#3%(4(56#\n\n1@>8:?.1@>?.@A.1=>2#3+4*56#\n\n1@>8:?.1@>E@?2.<A-#3%(4%56#\n\n1@>8:?.A?@2D2.1@>?#3%4*56#\n\n/8A>98FG8E.92/09?@D2#3$4!56#\n\n/9=:>8.<2?#3$4)56#\n\n/9=:>8.7-(#3%4&56#\n\n/9=:>8.?;<$#3(4,56#\n\n-<>2.B897<>.>8H2#3$4$56#\n\n-<>2.B897<>.5:<91#3$4!56#\n\n7<>;./89-@/#3'4,56#\n\n7<>;.:<9I<F.?07?#3(4,56#\n\n7<>;.?:2/>9<F.A897#3*4$56#\n\n92J25:.-A<#3'4%56#\n\n?>9@AJ.1<?2)'#3%4(56#\n\n?>9@AJ.B<?><#3$4(56#\n\n?>9@AJ.><J/F80-#3$4$56#\n\n?>9@AJ.0A:</C./8-2#3$4%56#\n\n?>9@AJ.D<F@-<>2.@A:0>#3$4,56#\n\nKA>29:92># L<ID2#\n\nFigure 11. Fraction of dynamic bytecodes executed by inter-\npreter and on native traces. The speedup vs. interpreter is shown\nin parentheses next to each test. The fraction of bytecodes exe-\ncuted while recording is too small to see in this figure, except\nfor crypto-md5, where fully 3% of bytecodes are executed while\nrecording. In most of the tests, almost all the bytecodes are exe-\ncuted by compiled traces. Three of the benchmarks are not traced\nat all and run in the interpreter.\n\nloops and heavily branching code, and a specialized fuzz tester in-\ndeed revealed several regressions which we subsequently corrected.\n\n7. Evaluation\nWe evaluated our JavaScript tracing implementation using Sun-\nSpider, the industry standard JavaScript benchmark suite. SunSpi-\nder consists of 26 short-running (less than 250ms, average 26ms)\nJavaScript programs. This is in stark contrast to benchmark suites\nsuch as SpecJVM98 (3) used to evaluate desktop and server Java\nVMs. Many programs in those benchmarks use large data sets and\nexecute for minutes. The SunSpider programs carry out a variety of\ntasks, primarily 3d rendering, bit-bashing, cryptographic encoding,\nmath kernels, and string processing.\n\nAll experiments were performed on a MacBook Pro with 2.2\nGHz Core 2 processor and 2 GB RAM running MacOS 10.5.\n\nBenchmark results. The main question is whether programs\nrun faster with tracing. For this, we ran the standard SunSpider test\ndriver, which starts a JavaScript interpreter, loads and runs each\nprogram once for warmup, then loads and runs each program 10\ntimes and reports the average time taken by each. We ran 4 differ-\nent configurations for comparison: (a) SpiderMonkey, the baseline\ninterpreter, (b) TraceMonkey, (d) SquirrelFish Extreme (SFX), the\ncall-threaded JavaScript interpreter used in Apple’s WebKit, and\n(e) V8, the method-compiling JavaScript VM from Google.\n\nFigure 10 shows the relative speedups achieved by tracing, SFX,\nand V8 against the baseline (SpiderMonkey). Tracing achieves the\nbest speedups in integer-heavy benchmarks, up to the 25x speedup\non bitops-bitwise-and.\n\nTraceMonkey is the fastest VM on 9 of the 26 benchmarks\n(3d-morph, bitops-3bit-bits-in-byte, bitops-bitwise-\nand, crypto-sha1, math-cordic, math-partial-sums, math-\nspectral-norm, string-base64, string-validate-input).\n\n\n\n!\"\n\n#\"\n\n$!\"\n\n$#\"\n\n%!\"\n\n%#\"\n\n&'()*+,\"\n\n-./\"\n\n01\"\n\nFigure 10. Speedup vs. a baseline JavaScript interpreter (SpiderMonkey) for our trace-based JIT compiler, Apple’s SquirrelFish Extreme\ninline threading interpreter and Google’s V8 JS compiler. Our system generates particularly efficient code for programs that benefit most from\ntype specialization, which includes SunSpider Benchmark programs that perform bit manipulation. We type-specialize the code in question\nto use integer arithmetic, which substantially improves performance. For one of the benchmark programs we execute 25 times faster than\nthe SpiderMonkey interpreter, and almost 5 times faster than V8 and SFX. For a large number of benchmarks all three VMs produce similar\nresults. We perform worst on benchmark programs that we do not trace and instead fall back onto the interpreter. This includes the recursive\nbenchmarks access-binary-trees and control-flow-recursive, for which we currently don’t generate any native code.\n\nIn particular, the bitops benchmarks are short programs that per-\nform many bitwise operations, so TraceMonkey can cover the en-\ntire program with 1 or 2 traces that operate on integers. TraceMon-\nkey runs all the other programs in this set almost entirely as native\ncode.\n\nregexp-dna is dominated by regular expression matching,\nwhich is implemented in all 3 VMs by a special regular expression\ncompiler. Thus, performance on this benchmark has little relation\nto the trace compilation approach discussed in this paper.\n\nTraceMonkey’s smaller speedups on the other benchmarks can\nbe attributed to a few specific causes:\n\n• The implementation does not currently trace recursion, so\nTraceMonkey achieves a small speedup or no speedup on\nbenchmarks that use recursion extensively: 3d-cube, 3d-\nraytrace, access-binary-trees, string-tagcloud, and\ncontrolflow-recursive.\n\n• The implementation does not currently trace eval and some\nother functions implemented in C. Because date-format-\ntofte and date-format-xparb use such functions in their\nmain loops, we do not trace them.\n\n• The implementation does not currently trace through regular\nexpression replace operations. The replace function can be\npassed a function object used to compute the replacement text.\nOur implementation currently does not trace functions called\nas replace functions. The run time of string-unpack-code is\ndominated by such a replace call.\n\n• Two programs trace well, but have a long compilation time.\naccess-nbody forms a large number of traces (81). crypto-md5\nforms one very long trace. We expect to improve performance\non this programs by improving the compilation speed of nano-\njit.\n\n• Some programs trace very well, and speed up compared to\nthe interpreter, but are not as fast as SFX and/or V8, namely\nbitops-bits-in-byte, bitops-nsieve-bits, access-\nfannkuch, access-nsieve, and crypto-aes. The reason is\nnot clear, but all of these programs have nested loops with\nsmall bodies, so we suspect that the implementation has a rela-\ntively high cost for calling nested traces. string-fasta traces\nwell, but its run time is dominated by string processing builtins,\nwhich are unaffected by tracing and seem to be less efficient in\nSpiderMonkey than in the two other VMs.\n\nDetailed performance metrics. In Figure 11 we show the frac-\ntion of instructions interpreted and the fraction of instructions exe-\ncuted as native code. This figure shows that for many programs, we\nare able to execute almost all the code natively.\n\nFigure 12 breaks down the total execution time into four activ-\nities: interpreting bytecodes while not recording, recording traces\n(including time taken to interpret the recorded trace), compiling\ntraces to native code, and executing native code traces.\n\nThese detailed metrics allow us to estimate parameters for a\nsimple model of tracing performance. These estimates should be\nconsidered very rough, as the values observed on the individual\nbenchmarks have large standard deviations (on the order of the\n\n\n\nLoops Trees Traces Aborts Flushes Trees/Loop Traces/Tree Traces/Loop Speedup\n3d-cube 25 27 29 3 0 1.1 1.1 1.2 2.20x\n3d-morph 5 8 8 2 0 1.6 1.0 1.6 2.86x\n3d-raytrace 10 25 100 10 1 2.5 4.0 10.0 1.18x\naccess-binary-trees 0 0 0 5 0 - - - 0.93x\naccess-fannkuch 10 34 57 24 0 3.4 1.7 5.7 2.20x\naccess-nbody 8 16 18 5 0 2.0 1.1 2.3 4.19x\naccess-nsieve 3 6 8 3 0 2.0 1.3 2.7 3.05x\nbitops-3bit-bits-in-byte 2 2 2 0 0 1.0 1.0 1.0 25.47x\nbitops-bits-in-byte 3 3 4 1 0 1.0 1.3 1.3 8.67x\nbitops-bitwise-and 1 1 1 0 0 1.0 1.0 1.0 25.20x\nbitops-nsieve-bits 3 3 5 0 0 1.0 1.7 1.7 2.75x\ncontrolflow-recursive 0 0 0 1 0 - - - 0.98x\ncrypto-aes 50 72 78 19 0 1.4 1.1 1.6 1.64x\ncrypto-md5 4 4 5 0 0 1.0 1.3 1.3 2.30x\ncrypto-sha1 5 5 10 0 0 1.0 2.0 2.0 5.95x\ndate-format-tofte 3 3 4 7 0 1.0 1.3 1.3 1.07x\ndate-format-xparb 3 3 11 3 0 1.0 3.7 3.7 0.98x\nmath-cordic 2 4 5 1 0 2.0 1.3 2.5 4.92x\nmath-partial-sums 2 4 4 1 0 2.0 1.0 2.0 5.90x\nmath-spectral-norm 15 20 20 0 0 1.3 1.0 1.3 7.12x\nregexp-dna 2 2 2 0 0 1.0 1.0 1.0 4.21x\nstring-base64 3 5 7 0 0 1.7 1.4 2.3 2.53x\nstring-fasta 5 11 15 6 0 2.2 1.4 3.0 1.49x\nstring-tagcloud 3 6 6 5 0 2.0 1.0 2.0 1.09x\nstring-unpack-code 4 4 37 0 0 1.0 9.3 9.3 1.20x\nstring-validate-input 6 10 13 1 0 1.7 1.3 2.2 1.86x\n\nFigure 13. Detailed trace recording statistics for the SunSpider benchmark set.\n\nmean). We exclude regexp-dna from the following calculations,\nbecause most of its time is spent in the regular expression matcher,\nwhich has much different performance characteristics from the\nother programs. (Note that this only makes a difference of about\n10% in the results.) Dividing the total execution time in processor\nclock cycles by the number of bytecodes executed in the base\ninterpreter shows that on average, a bytecode executes in about\n35 cycles. Native traces take about 9 cycles per bytecode, a 3.9x\nspeedup over the interpreter.\n\nUsing similar computations, we find that trace recording takes\nabout 3800 cycles per bytecode, and compilation 3150 cycles per\nbytecode. Hence, during recording and compiling the VM runs at\n1/200 the speed of the interpreter. Because it costs 6950 cycles to\ncompile a bytecode, and we save 26 cycles each time that code is\nrun natively, we break even after running a trace 270 times.\n\nThe other VMs we compared with achieve an overall speedup\nof 3.0x relative to our baseline interpreter. Our estimated native\ncode speedup of 3.9x is significantly better. This suggests that\nour compilation techniques can generate more efficient native code\nthan any other current JavaScript VM.\n\nThese estimates also indicate that our startup performance could\nbe substantially better if we improved the speed of trace recording\nand compilation. The estimated 200x slowdown for recording and\ncompilation is very rough, and may be influenced by startup factors\nin the interpreter (e.g., caches that have not warmed up yet during\nrecording). One observation supporting this conjecture is that in\nthe tracer, interpreted bytecodes take about 180 cycles to run. Still,\nrecording and compilation are clearly both expensive, and a better\nimplementation, possibly including redesign of the LIR abstract\nsyntax or encoding, would improve startup performance.\n\nOur performance results confirm that type specialization using\ntrace trees substantially improves performance. We are able to\noutperform the fastest available JavaScript compiler (V8) and the\n\nfastest available JavaScript inline threaded interpreter (SFX) on 9\nof 26 benchmarks.\n\n8. Related Work\nTrace optimization for dynamic languages. The closest area of\nrelated work is on applying trace optimization to type-specialize\ndynamic languages. Existing work shares the idea of generating\ntype-specialized code speculatively with guards along interpreter\ntraces.\n\nTo our knowledge, Rigo’s Psyco (16) is the only published\ntype-specializing trace compiler for a dynamic language (Python).\nPsyco does not attempt to identify hot loops or inline function calls.\nInstead, Psyco transforms loops to mutual recursion before running\nand traces all operations.\n\nPall’s LuaJIT is a Lua VM in development that uses trace com-\npilation ideas. (1). There are no publications on LuaJIT but the cre-\nator has told us that LuaJIT has a similar design to our system, but\nwill use a less aggressive type speculation (e.g., using a floating-\npoint representation for all number values) and does not generate\nnested traces for nested loops.\n\nGeneral trace optimization. General trace optimization has\na longer history that has treated mostly native code and typed\nlanguages like Java. Thus, these systems have focused less on type\nspecialization and more on other optimizations.\n\nDynamo (7) by Bala et al, introduced native code tracing as a\nreplacement for profile-guided optimization (PGO). A major goal\nwas to perform PGO online so that the profile was specific to\nthe current execution. Dynamo used loop headers as candidate hot\ntraces, but did not try to create loop traces specifically.\n\nTrace trees were originally proposed by Gal et al. (11) in the\ncontext of Java, a statically typed language. Their trace trees ac-\ntually inlined parts of outer loops within the inner loops (because\n\n\n\n!\"# $!\"# %!\"# &!\"# '!\"# (!!\"#\n\n)*+,-./#0$1$23#\n\n)*+45678#0$1923#\n\n)*+6:;<6:,/#0(1$23#\n\n:,,/==+.>?:6;+<6//=#0!1923#\n\n:,,/==+@:??A-,8#0$1$23#\n\n:,,/==+?.5*;#0%1$23#\n\n:,,/==+?=>/B/#0)1!23#\n\n.><57=+).><+.><=+>?+.;</#0$C1C23#\n\n.><57=+.><=+>?+.;</#0'1D23#\n\n.><57=+.><E>=/+:?*#0$C1$23#\n\n.><57=+?=>/B/+.><=#0$1D23#\n\n,5?<65FG5E+6/,-6=>B/#0(1!23#\n\n,6;7<5+:/=#0(1&23#\n\n,6;7<5+4*C#0$1)23#\n\n,6;7<5+=8:(#0C1923#\n\n*:</+@564:<+<5H/#0(1(23#\n\n*:</+@564:<+27:6.#0(1!23#\n\n4:<8+,56*>,#0%1923#\n\n4:<8+7:6I:F+=-4=#0C1923#\n\n4:<8+=7/,<6:F+?564#0D1(23#\n\n6/J/27+*?:#0%1$23#\n\n=<6>?J+.:=/&%#0$1C23#\n\n=<6>?J+@:=<:#0(1C23#\n\n=<6>?J+<:J,F5-*#0(1(23#\n\n=<6>?J+-?7:,A+,5*/#0(1$23#\n\n=<6>?J+B:F>*:</+>?7-<#0(1923#\n\nK?</676/<# L5?><56# M/,56*# N547>F/# N:FF#O6:,/# M-?#O6:,/#\n\nFigure 12. Fraction of time spent on major VM activities. The\nspeedup vs. interpreter is shown in parentheses next to each test.\nMost programs where the VM spends the majority of its time run-\nning native code have a good speedup. Recording and compilation\ncosts can be substantial; speeding up those parts of the implemen-\ntation would improve SunSpider performance.\n\ninner loops become hot first), leading to much greater tail duplica-\ntion.\n\nYETI, from Zaleski et al. (19) applied Dynamo-style tracing\nto Java in order to achieve inlining, indirect jump elimination,\nand other optimizations. Their primary focus was on designing an\ninterpreter that could easily be gradually re-engineered as a tracing\nVM.\n\nSuganuma et al. (18) described region-based compilation (RBC),\na relative of tracing. A region is an subprogram worth optimizing\nthat can include subsets of any number of methods. Thus, the com-\npiler has more flexibility and can potentially generate better code,\nbut the profiling and compilation systems are correspondingly more\ncomplex.\n\nType specialization for dynamic languages. Dynamic lan-\nguage implementors have long recognized the importance of type\nspecialization for performance. Most previous work has focused on\nmethods instead of traces.\n\nChambers et. al (9) pioneered the idea of compiling multiple\nversions of a procedure specialized for the input types in the lan-\nguage Self. In one implementation, they generated a specialized\nmethod online each time a method was called with new input types.\nIn another, they used an offline whole-program static analysis to\ninfer input types and constant receiver types at call sites. Interest-\ningly, the two techniques produced nearly the same performance.\n\nSalib (17) designed a type inference algorithm for Python based\non the Cartesian Product Algorithm and used the results to special-\nize on types and translate the program to C++.\n\nMcCloskey (14) has work in progress based on a language-\nindependent type inference that is used to generate efficient C\nimplementations of JavaScript and Python programs.\n\nNative code generation by interpreters. The traditional inter-\npreter design is a virtual machine that directly executes ASTs or\nmachine-code-like bytecodes. Researchers have shown how to gen-\n\nerate native code with nearly the same structure but better perfor-\nmance.\n\nCall threading, also known as context threading (8), compiles\nmethods by generating a native call instruction to an interpreter\nmethod for each interpreter bytecode. A call-return pair has been\nshown to be a potentially much more efficient dispatch mechanism\nthan the indirect jumps used in standard bytecode interpreters.\n\nInline threading (15) copies chunks of interpreter native code\nwhich implement the required bytecodes into a native code cache,\nthus acting as a simple per-method JIT compiler that eliminates the\ndispatch overhead.\n\nNeither call threading nor inline threading perform type special-\nization.\n\nApple’s SquirrelFish Extreme (5) is a JavaScript implementa-\ntion based on call threading with selective inline threading. Com-\nbined with efficient interpreter engineering, these threading tech-\nniques have given SFX excellent performance on the standard Sun-\nSpider benchmarks.\n\nGoogle’s V8 is a JavaScript implementation primarily based\non inline threading, with call threading only for very complex\noperations.\n\n9. Conclusions\nThis paper described how to run dynamic languages efficiently by\nrecording hot traces and generating type-specialized native code.\nOur technique focuses on aggressively inlined loops, and for each\nloop, it generates a tree of native code traces representing the\npaths and value types through the loop observed at run time. We\nexplained how to identify loop nesting relationships and generate\nnested traces in order to avoid excessive code duplication due\nto the many paths through a loop nest. We described our type\nspecialization algorithm. We also described our trace compiler,\nwhich translates a trace from an intermediate representation to\noptimized native code in two linear passes.\n\nOur experimental results show that in practice loops typically\nare entered with only a few different combinations of value types\nof variables. Thus, a small number of traces per loop is sufficient\nto run a program efficiently. Our experiments also show that on\nprograms amenable to tracing, we achieve speedups of 2x to 20x.\n\n10. Future Work\nWork is underway in a number of areas to further improve the\nperformance of our trace-based JavaScript compiler. We currently\ndo not trace across recursive function calls, but plan to add the\nsupport for this capability in the near term. We are also exploring\nadoption of the existing work on tree recompilation in the context\nof the presented dynamic compiler in order to minimize JIT pause\ntimes and obtain the best of both worlds, fast tree stitching as well\nas the improved code quality due to tree recompilation.\n\nWe also plan on adding support for tracing across regular ex-\npression substitutions using lambda functions, function applica-\ntions and expression evaluation using eval. All these language\nconstructs are currently executed via interpretation, which limits\nour performance for applications that use those features.\n\nAcknowledgments\nParts of this effort have been sponsored by the National Science\nFoundation under grants CNS-0615443 and CNS-0627747, as well\nas by the California MICRO Program and industrial sponsor Sun\nMicrosystems under Project No. 07-127.\n\nThe U.S. Government is authorized to reproduce and distribute\nreprints for Governmental purposes notwithstanding any copyright\nannotation thereon. Any opinions, findings, and conclusions or rec-\nommendations expressed here are those of the author and should\n\n\n\nnot be interpreted as necessarily representing the official views,\npolicies or endorsements, either expressed or implied, of the Na-\ntional Science foundation (NSF), any other agency of the U.S. Gov-\nernment, or any of the companies mentioned above.\n\nReferences\n[1] LuaJIT roadmap 2008 - http://lua-users.org/lists/lua-l/2008-\n\n02/msg00051.html.\n[2] Mozilla — Firefox web browser and Thunderbird email client -\n\nhttp://www.mozilla.com.\n[3] SPECJVM98 - http://www.spec.org/jvm98/.\n[4] SpiderMonkey (JavaScript-C) Engine -\n\nhttp://www.mozilla.org/js/spidermonkey/.\n[5] Surfin’ Safari - Blog Archive - Announcing SquirrelFish Extreme -\n\nhttp://webkit.org/blog/214/introducing-squirrelfish-extreme/.\n[6] A. Aho, R. Sethi, J. Ullman, and M. Lam. Compilers: Principles,\n\ntechniques, and tools, 2006.\n[7] V. Bala, E. Duesterwald, and S. Banerjia. Dynamo: A transparent\n\ndynamic optimization system. In Proceedings of the ACM SIGPLAN\nConference on Programming Language Design and Implementation,\npages 1–12. ACM Press, 2000.\n\n[8] M. Berndl, B. Vitale, M. Zaleski, and A. Brown. Context Threading:\na Flexible and Efficient Dispatch Technique for Virtual Machine In-\nterpreters. In Code Generation and Optimization, 2005. CGO 2005.\nInternational Symposium on, pages 15–26, 2005.\n\n[9] C. Chambers and D. Ungar. Customization: Optimizing Compiler\nTechnology for SELF, a Dynamically-Typed O bject-Oriented Pro-\ngramming Language. In Proceedings of the ACM SIGPLAN 1989\nConference on Programming Language Design and Implementation,\npages 146–160. ACM New York, NY, USA, 1989.\n\n[10] A. Gal. Efficient Bytecode Verification and Compilation in a Virtual\nMachine Dissertation. PhD thesis, University Of California, Irvine,\n2006.\n\n[11] A. Gal, C. W. Probst, and M. Franz. HotpathVM: An effective JIT\ncompiler for resource-constrained devices. In Proceedings of the\nInternational Conference on Virtual Execution Environments, pages\n144–153. ACM Press, 2006.\n\n[12] C. Garrett, J. Dean, D. Grove, and C. Chambers. Measurement and\nApplication of Dynamic Receiver Class Distributions. 1994.\n\n[13] J. Ha, M. R. Haghighat, S. Cong, and K. S. McKinley. A concurrent\ntrace-based just-in-time compiler for javascript. Dept.of Computer\nSciences, The University of Texas at Austin, TR-09-06, 2009.\n\n[14] B. McCloskey. Personal communication.\n[15] I. Piumarta and F. Riccardi. Optimizing direct threaded code by selec-\n\ntive inlining. In Proceedings of the ACM SIGPLAN 1998 conference\non Programming language design and implementation, pages 291–\n300. ACM New York, NY, USA, 1998.\n\n[16] A. Rigo. Representation-Based Just-In-time Specialization and the\nPsyco Prototype for Python. In PEPM, 2004.\n\n[17] M. Salib. Starkiller: A Static Type Inferencer and Compiler for\nPython. In Master’s Thesis, 2004.\n\n[18] T. Suganuma, T. Yasue, and T. Nakatani. A Region-Based Compila-\ntion Technique for Dynamic Compilers. ACM Transactions on Pro-\ngramming Languages and Systems (TOPLAS), 28(1):134–174, 2006.\n\n[19] M. Zaleski, A. D. Brown, and K. Stoodley. YETI: A graduallY\nExtensible Trace Interpreter. In Proceedings of the International\nConference on Virtual Execution Environments, pages 83–93. 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