Improving both the performance benefits and speed of optimization phase sequence searches

Kulkarni, Prasad A.; Jantz, Michael R.; Whalley, David

doi:10.1145/1755888.1755903

Cited by 11 publications

(11 citation statements)

References 38 publications

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“…Prior research has found that optimization phase sequences tuned to each method yield better program performance than a single program-wide phase sequence [1,18]. In this section, we explore the performance potential of optimization selection at the method-level during dynamic JIT compilation.…”

Section: Method-specific Genetic Algorithmmentioning

confidence: 99%

“…Also related is their more recent work that compares the ability of GA-based program and function-level searches to find the best phase sequence, and finds the finer-granularity of function-level searches to achieve better overall results in their static C compiler, VPO [18]. All these above studies were conducted for static compilers.…”

Section: Background and Related Workmentioning

confidence: 99%

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Performance potential of optimization phase selection during dynamic JIT compilation

Jantz

Kulkarni

2013

Proceedings of the 9th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

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Phase selection is the process of customizing the applied set of compiler optimization phases for individual functions or programs to improve performance of generated code. Researchers have recently developed novel feature-vector based heuristic techniques to perform phase selection during online JIT compilation. While these heuristics improve program startup speed, steady-state performance was not seen to benefit over the default fixed single sequence baseline. Unfortunately, it is still not conclusively known whether this lack of steady-state performance gain is due to a failure of existing online phase selection heuristics, or because there is, indeed, little or no speedup to be gained by phase selection in online JIT environments. The goal of this work is to resolve this question, while examining the phase selection related behavior of optimizations, and assessing and improving the effectiveness of existing heuristic solutions.We conduct experiments to find and understand the potency of the factors that can cause the phase selection problem in JIT compilers. Next, using long-running genetic algorithms we determine that program-wide and method-specific phase selection in the HotSpot JIT compiler can produce ideal steady-state performance gains of up to 15% (4.3% average) and 44% (6.2% average) respectively. We also find that existing state-of-the-art heuristic solutions are unable to realize these performance gains (in our experimental setup), discuss possible causes, and show that exploiting knowledge of optimization phase behavior can help improve such heuristic solutions. Our work develops a robust open-source productionquality framework using the HotSpot JVM to further explore this problem in the future.

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Section: Method-specific Genetic Algorithmmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Performance potential of optimization phase selection during dynamic JIT compilation

Jantz

Kulkarni

2013

Proceedings of the 9th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

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“…Moreover, their cache warmup system is limited because it does not use a memory trace and is based on the execution of previous iterations. Similarly, Kulkarni et al 39 propose a piecewise search at the function level granularity. They propose a perfunction compilation using the VPO compiler framework.…”

Section: Application Reduction and Fine Grained Tuningmentioning

confidence: 99%

Piecewise holistic autotuning of parallel programs with CERE

Popov

Akel²,

Chatelain

et al. 2017

Concurrency and Computation

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Summary Current architecture complexity requires fine tuning of compiler and runtime parameters to achieve best performance. Autotuning substantially improves default parameters in many scenarios, but it is a costly process requiring long iterative evaluations. We propose an automatic piecewise autotuner based on CERE (Codelet Extractor and REplayer). CERE decomposes applications into small pieces called codelets: Each codelet maps to a loop or to an OpenMP parallel region and can be replayed as a standalone program. Codelet autotuning achieves better speedups at a lower tuning cost. By grouping codelet invocations with the same performance behavior, CERE reduces the number of loops or OpenMP regions to be evaluated. Moreover, unlike whole‐program tuning, CERE customizes the set of best parameters for each specific OpenMP region or loop. We demonstrate the CERE tuning of compiler optimizations, number of threads, thread affinity, and scheduling policy on both nonuniform memory access and heterogeneous architectures. Over the NAS benchmarks, we achieve an average speedup of 1.08× after tuning. Tuning a codelet is 13× cheaper than whole‐program evaluation and predicts the tuning impact with a 94.7% accuracy. Similarly, exploring thread configurations and scheduling policies for a Black‐Scholes solver on an heterogeneous big.LITTLE architecture is over 40× faster using CERE.

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“…The work in [20] uses several machine learning algorithms to find the best sequence of optimization passes, observing that search techniques such as GAs achieve gains very close to best performance. This work was extended in [25] where they compare the ability of GAbased and function-level searches to find the best optimization sequences for their VPO compiler. The reuse methodology proposed in [26] uses generic programming to incorporate user-defined optimizations into the compiler.…”

Section: Related Workmentioning

confidence: 99%

Exploration of compiler optimization sequences using clustering-based selection

et al. 2014

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Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e.g., function). As this exploration is a complex and time consuming task, in this paper we present DSE strategies to select optimization sequences to both improve the performance of each function and reduce the exploration time. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic code representation of the source code. The DSE process uses the reduced set identified by clustering in two ways: as the design space or as the initial configuration. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences considering 49 compilation passes and targeting a Xilinx MicroBlaze processor, and were performed aiming performance improvements for 41 functions. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space (18× over a Genetic Algorithm approach for DSE) at the same time that important performance speedups (43% over the baseline) were obtained by the optimized codes.

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Improving both the performance benefits and speed of optimization phase sequence searches

Cited by 11 publications

References 38 publications

Performance potential of optimization phase selection during dynamic JIT compilation

Performance potential of optimization phase selection during dynamic JIT compilation

Piecewise holistic autotuning of parallel programs with CERE

Exploration of compiler optimization sequences using clustering-based selection

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