Reducing trace selection footprint for large-scale Java applications without performance loss

Wu, Peng; Hayashizaki, Hiroshige; Inoue, Hiroshi; Nakatani, Toshio

doi:10.1145/2048066.2048127

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…We implemented our adaptive multi-level compilation scheme with the TTgraph-based trace selection in our trace-JIT [1,6,10], which is based on the IBM J9/TR Java VM and JIT compiler [3]. Figure 8 shows a component view of our trace-JIT.…”

Section: Methodsmentioning

confidence: 99%

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Adaptive multi-level compilation in a trace-based Java JIT compiler

Inoue

Hayashizaki

et al. 2012

Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications

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This paper describes our multi-level compilation techniques implemented in a trace-based Java JIT compiler (trace-JIT). Like existing multi-level compilation for method-based compilers, we start JIT compilation with a small compilation scope and a low optimization level so the program can start running quickly. Then we identify hot paths with a timer-based sampling profiler, generate long traces that capture the hot paths, and recompile them with a high optimization level to improve the peak performance. A key to high performance is selecting long traces that effectively capture the entire hot paths for upgrade recompilations. To do this, we introduce a new technique to generate a directed graph representing the control flow, a TTgraph, and use the TTgraph in the trace selection engine to efficiently select long traces. We show that our multilevel compilation improves the peak performance of programs by up to 58.5% and 22.2% on average compared to compiling all of the traces only at a low optimization level. Comparing the performance with our multi-level compilation to the performance when compiling all of the traces at a high optimization level, our technique can reduce the startup times of programs by up to 61.1% and 31.3% on average without significant reduction in the peak performance. Our results show that our adaptive multilevel compilation can balance the peak performance and startup time by taking advantage of different optimization levels.

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Section: Methodsmentioning

confidence: 99%

“…However, generating long traces often causes more duplication among traces. The duplication significantly increases code size and compilation times and results in poor startup performance [1].…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi-level compilation in a trace-based Java JIT compiler

Inoue

Hayashizaki

et al. 2012

Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications

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“…Dynamo pioneered many early concepts of trace formation and trace runtime management. Many DBT systems [6,7,13,25] and just-in-time compilers [10,16,26] use NET or its variants to form traces.…”

Section: Dynamic Optimization Systemsmentioning

confidence: 99%

“…Recently, trace-based compilation has gained popularity in dynamic scripting languages [5,10] and high level language virtual machines [11,16,17,26]. Wu et al [26] and Inoue et al [16,17] investigate the performance of several variations of NET on tracebased Java virtual machines.…”

Section: Trace-based Language Virtual Machinesmentioning

confidence: 99%

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Improving dynamic binary optimization through early-exit guided code region formation

et al. 2013

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Most dynamic binary translators (DBT) and optimizers (DBO) target binary traces, i.e. frequently executed paths, as code regions to be translated and optimized. Code region formation is the most important first step in all DBTs and DBOs. The quality of the dynamically formed code regions determines the extent and the types of optimization opportunities that can be exposed to DBTs and DBOs, and thus, determines the ultimate quality of the final optimized code. The Next-Executing-Tail (NET) trace formation method used in HP Dynamo is an early example of such techniques. Many existing trace formation schemes are variants of NET. They work very well for most binary traces, but they also suffer a major problem: the formed traces may contain a large number of early exits that could be branched out during the execution. If this happens frequently, the program execution will spend more time in the slow binary interpreter or in the unoptimized code regions than in the optimized traces in code cache. The benefit of the trace optimization is thus lost. Traces/regions with frequently taken early-exits are called delinquent traces/regions. Our empirical study shows that at least 8 of the 12 SPEC CPU2006 integer benchmarks have delinquent traces.In this paper, we propose a light-weight region formation technique called Early-Exit Guided Region Formation (EEG) to improve the quality of the formed traces/regions. It iteratively identifies and merges delinquent regions into larger code regions. We have implemented our EEG algorithm in two LLVM-based multithreaded DBTs targeting ARM and IA32 instruction set architecture (ISA), respectively. Using SPEC CPU2006 benchmark suite with reference inputs, our results show that compared to an NETvariant currently used in QEMU, a state-of-the-art retargetable DBT, EEG can achieve a significant performance improvement of Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. . . . $15.00 up to 72% (27% on average), and to 49% (23% on average) for IA32 and ARM, respectively.

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Context-sensitive trace inlining for Java

Häubl¹,

Wimmer²,

Mössenböck³

2013

Computer Languages, Systems & Structures

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Method inlining is one of the most important optimizations in method-based just-in-time (JIT) compilers. It widens the compilation scope and therefore allows optimizing multiple methods as a whole, which increases the performance. However, if method inlining is used too frequently, the compilation time increases and too much machine code is generated. This has negative effects on the performance.Trace-based JIT compilers only compile frequently executed paths, so-called traces, instead of whole methods. This may result in faster compilation, less generated machine code, and better optimized machine code. In the previous work, we implemented a trace recording infrastructure and a trace-based compiler for JavaTM, by modifying the Java HotSpot VM. Based on this work, we evaluate the effect of trace inlining on the performance and the amount of generated machine code.Trace inlining has several major advantages when compared to method inlining. First, trace inlining is more selective than method inlining, because only frequently executed paths are inlined. Second, the recorded traces may capture information about virtual calls, which simplify inlining. A third advantage is that trace information is context sensitive so that different method parts can be inlined depending on the specific call site. These advantages allow more aggressive inlining while the amount of generated machine code is still reasonable.We evaluate several inlining heuristics on the benchmark suites DaCapo 9.12 Bach, SPECjbb2005, and SPECjvm2008 and show that our trace-based compiler achieves an up to 51% higher peak performance than the method-based Java HotSpot client compiler. Furthermore, we show that the large compilation scope of our trace-based compiler has a positive effect on other compiler optimizations such as constant folding or null check elimination.

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Reducing trace selection footprint for large-scale Java applications without performance loss

Cited by 10 publications

References 16 publications

Adaptive multi-level compilation in a trace-based Java JIT compiler

Adaptive multi-level compilation in a trace-based Java JIT compiler

Improving dynamic binary optimization through early-exit guided code region formation

Context-sensitive trace inlining for Java

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