Da capo con scala

SeweAndreas,; MeziniMira,; SarimbekovAibek,; BinderWalter,

doi:10.1145/2076021.2048118

Cited by 19 publications

(2 citation statements)

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“…The subsequently proposed ScalaBench suite [71,72] identified a range of typical Scala programs, and argued that Scala and Java programs have considerably different distributions of instructions, polymorphic calls, object allocations, and method sizes. This observation that benchmark suites tend to over-represent certain programming styles was also noticed in other languages, (e.g., JavaScript [69]).…”

Section: Related Workmentioning

confidence: 99%

On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity

Prokopec¹,

Rosà²,

Leopoldseder³

et al. 2019

Preprint

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The recently proposed Renaissance suite is composed of modern, real-world, concurrent, and object-oriented workloads that exercise various concurrency primitives of the JVM. Renaissance was used to compare performance of two stateof-the-art, production-quality JIT compilers (HotSpot C2 and Graal), and to show that the performance differences are more significant than on existing suites such as DaCapo and SPECjvm2008.In this technical report, we give an overview of the experimental setup that we used to assess the variety and complexity of the Renaissance suite, as well as its amenability to new compiler optimizations. We then present the obtained measurements in detail. CCS Concepts• Software and its engineering → General programming languages; • Social and professional topics → History of programming languages;

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Section: Related Workmentioning

confidence: 99%

On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity

Prokopec¹,

Rosà²,

Leopoldseder³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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“…We evaluated our results using the DaCapo 9.12 [Blackburn et al, 2006] as well as the Scala-DaCapo [Sewe et al, 2011] benchmark suites. We excluded the eclipse, tomcat, tradebeans, and tradesoap benchmarks from DaCapo due to Java 8 compatibility issues.…”

Section: Benchmark Suitesmentioning

confidence: 99%

Trace Register Allocation Policies

Eisl

Marr

Würthinger³

et al. 2017

Proceedings of the 14th International Conference on Managed Languages and Runtimes

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Register allocation is an integral part of compilation, regardless of whether a compiler aims for fast compilation or optimal code quality. State-of-the-art dynamic compilers often use global register allocation approaches such as linear scan. Recent results suggest that non-global trace-based register allocation approaches can compete with global approaches in terms of allocation quality. Instead of processing the whole compilation unit (i.e., method) at once, a trace-based register allocator divides the problem into linear code segments, called traces.In this work, we present a register allocation framework that can exploit the additional exibility of traces to select di erent allocation strategies based on the characteristics of a trace. This provides us with ne-grained control over the trade-o between compile time and peak performance in a just-in-time compiler.Our framework features three allocation strategies: a linearscan-based approach that achieves good code quality, a single-pass bottom-up strategy that aims for short allocation times, and an allocator for trivial traces.To demonstrate the exibility of the framework, we select 8 allocation policies and show their impact on compile time and peak performance. This approach can reduce allocation time by 7%-43% at a peak performance penalty of about 1%-11% on average.For systems that do not focus on peak performance, our approach allows to adjust the time spent for register allocation, and therefore the overall compilation time, thus nding the optimal balance between compile time and peak performance according to an application's requirements.

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RUGRAT: Evaluating program analysis and testing tools and compilers with large generated random benchmark applications

Hussain

Csallner

Grechanik

et al. 2014

Softw. Pract. Exper.

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Summary Benchmarks are heavily used in different areas of computer science to evaluate algorithms and tools. In program analysis and testing, open‐source and commercial programs are routinely used as benchmarks to evaluate different aspects of algorithms and tools. Unfortunately, many of these programs are written by programmers who introduce different biases, not to mention that it is very difficult to find programs that can serve as benchmarks with high reproducibility of results. We propose a novel approach for generating random benchmarks for evaluating program analysis and testing tools and compilers. Our approach uses stochastic parse trees, where language grammar production rules are assigned probabilities that specify the frequencies with which instantiations of these rules will appear in the generated programs. We implemented our tool for Java and applied it to generate a set of large benchmark programs of up to 5M lines of code each with which we evaluated different program analysis and testing tools and compilers. The generated benchmarks let us independently rediscover several issues in the evaluated tools. Copyright © 2014 John Wiley & Sons, Ltd.

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Da capo con scala

Cited by 19 publications

References 50 publications

On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity

On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity

Trace Register Allocation Policies

RUGRAT: Evaluating program analysis and testing tools and compilers with large generated random benchmark applications

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