Improving the performance of trace-based systems by false loop filtering

Hayashizaki, Hiroshige; Wu, Peng; Inoue, Hiroshi; Serrano, Mauricio J.; Nakatani, Toshio

doi:10.1145/2248487.1950412

Cited by 4 publications

(7 citation statements)

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“…In particular, Jolt's compiler adds lightweight instrumentation to the source of the application to identify the boundaries of loops, which can be difficult to identify accurately from a binary executable [15,34].…”

Section: The Jolt Systemmentioning

confidence: 99%

Detecting and Escaping Infinite Loops with Jolt

Carbin¹,

Misailovíc²,

Kling³

et al. 2011

Lecture Notes in Computer Science

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Abstract. Infinite loops can make applications unresponsive. Potential problems include lost work or output, denied access to application functionality, and a lack of responses to urgent events. We present Jolt, a novel system for dynamically detecting and escaping infinite loops. At the user's request, Jolt attaches to an application to monitor its progress. Specifically, Jolt records the program state at the start of each loop iteration. If two consecutive loop iterations produce the same state, Jolt reports to the user that the application is in an infinite loop. At the user's option, Jolt can then transfer control to a statement following the loop, thereby allowing the application to escape the infinite loop and ideally continue its productive execution. The immediate goal is to enable the application to execute long enough to save any pending work, finish any in-progress computations, or respond to any urgent events.We evaluated Jolt by applying it to detect and escape eight infinite loops in five benchmark applications. Jolt was able to detect seven of the eight infinite loops (the eighth changes the state on every iteration). We also evaluated the effect of escaping an infinite loop as an alternative to terminating the application. In all of our benchmark applications, escaping an infinite loop produced a more useful output than terminating the application. Finally, we evaluated how well escaping from an infinite loop approximated the correction that the developers later made to the application. For two out of our eight loops, escaping the infinite loop produced the same output as the corrected version of the application.

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Section: The Jolt Systemmentioning

confidence: 99%

Detecting and Escaping Infinite Loops with Jolt

Carbin¹,

Misailovíc²,

Kling³

et al. 2011

Lecture Notes in Computer Science

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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%

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

Inoue

Hayashizaki

et al. 2012

SIGPLAN Not.

Self Cite

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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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“…• Call Stack Comparison: To detect recursive functions, Hayashizaki et al (2011) inspect the call stack to determine when the target of the current function call is already on the stack.…”

Section: (Define (Dot U V) (For/sum ([X U] [Y V]) (* X Y)))mentioning

confidence: 99%

“…imprecise) in the presence of higherorder functions and the possibility of storing functions as values in the heap. In this section, we describe two runtime approaches to detect appropriate loops, both of which are dynamic variants of the "false loop filtering" technique (Hayashizaki et al 2011). …”

Section: Finding Loopsmentioning

confidence: 99%

See 1 more Smart Citation

Pycket: a tracing JIT for a functional language

Bauman

Bolz

Hirschfeld³

et al. 2015

SIGPLAN Not.

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We present Pycket, a high-performance tracing JIT compiler for Racket. Pycket supports a wide variety of the sophisticated features in Racket such as contracts, continuations, classes, structures, dynamic binding, and more. On average, over a standard suite of benchmarks, Pycket outperforms existing compilers, both Racket's JIT and other highly-optimizing Scheme compilers. Further, Pycket provides much better performance for proxies than existing systems, dramatically reducing the overhead of contracts and gradual typing. We validate this claim with performance evaluation on multiple existing benchmark suites.The Pycket implementation is of independent interest as an application of the RPython meta-tracing framework (originally created for PyPy), which automatically generates tracing JIT compilers from interpreters. Prior work on meta-tracing focuses on bytecode interpreters, whereas Pycket is a high-level interpreter based on the CEK abstract machine and operates directly on abstract syntax trees. Pycket supports proper tail calls and first-class continuations. In the setting of a functional language, where recursion and higher-order functions are more prevalent than explicit loops, the most significant performance challenge for a tracing JIT is identifying which control flows constitute a loop-we discuss two strategies for identifying loops and measure their impact.

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Improving the performance of trace-based systems by false loop filtering

Cited by 4 publications

References 0 publications

Detecting and Escaping Infinite Loops with Jolt

Detecting and Escaping Infinite Loops with Jolt

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

Pycket: a tracing JIT for a functional language

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