XJava: Exploiting Parallelism with Object-Oriented Stream Programming

Otto, Frank; Pankratius, Victor; Tichy, Walter F.

doi:10.1007/978-3-642-03869-3_81

“…XJava [24][25][26] is a Java extension also closely inspired by StreamIt. Its runtime exposes a small number of tuning parameters; [26] gives heuristics performing close to exhaustive search.…”

Section: Related Workmentioning

confidence: 99%

StreamJIT

Bosboom¹,

Rajadurai

²

,

Wong

³

et al. 2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

11

0

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There are many domain libraries, but despite the performance benefits of compilation, domain-specific languages are comparatively rare due to the high cost of implementing an optimizing compiler. We propose commensal compilation, a new strategy for compiling embedded domain-specific languages by reusing the massive investment in modern language virtual machine platforms. Commensal compilers use the host language's front-end, use host platform APIs that enable back-end optimizations by the host platform JIT, and use an autotuner for optimization selection. The cost of implementing a commensal compiler is only the cost of implementing the domain-specific optimizations. We demonstrate the concept by implementing a commensal compiler for the stream programming language StreamJIT atop the Java platform. Our compiler achieves performance 2.8 times better than the StreamIt native code (via GCC) compiler with considerably less implementation effort.

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“…PetaBricks [6] enables specifying different parallelization strategies and algorithms, so that the runtime environment can decide which implementation fits best for the current system load. XJava [14] is a language and runtime extension for Java which makes it specifically easy to develop object-oriented stream programs. MapReduce [15] is a very specific parallel pattern that enables software engineers to implement a parallel framework in a fast and less error prone way.…”

Section: Related Workmentioning

confidence: 99%

“…Programming Assistance ( [6], [13], [14], [15]): Parallelization can be done on different levels of abstraction. One way are libraries like STAPL [13] which is a collection of parallel data structures and algorithms.…”

Section: Related Workmentioning

confidence: 99%

Locating Parallelization Potential in Object-Oriented Data Structures

Molitorisz

¹

,

Karcher

²

,

Biele

³

et al. 2014

2014 IEEE 28th International Parallel and Distributed Processing Symposium

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The free lunch of ever increasing single-processor performance is over. Software engineers have to parallelize software to gain performance improvements. But not every software engineer is a parallel expert and with millions of lines of code that have not been developed with multicore in mind, we have to find ways to assist in identifying parallelization potential.This paper makes three contributions: 1) An empirical study of more than 900,000 lines of code reveals five use cases in the runtime profile of object-oriented data structures that carry parallelization potential. 2) The study also points out frequently used data structures in realistic software in which these use cases can be found. 3) We developed DSspy, an automatic dynamic profiler that locates these use cases and makes recommendations on how to parallelize them. Our evaluation shows that DSspy reduces the search space for parallelization by up to 77% and engineers only need to consider 23% of all data structure instances for parallelization.

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“…Other streaming languages have dropped static data rates in favor of dynamic fork-join parallelism, becoming more expressive at the cost of performance. XJava [24][25][26] is a Java extension also closely inspired by StreamIt. Its runtime exposes a small number of tuning parameters; [26] gives heuristics performing close to exhaustive search.…”

Section: Related Workmentioning

confidence: 99%

StreamJIT

Bosboom

¹

,

Rajadurai

²

,

Wong

³

et al. 2014

SIGPLAN Not.

4

0

View full text Add to dashboard Cite

There are many domain libraries, but despite the performance benefits of compilation, domain-specific languages are comparatively rare due to the high cost of implementing an optimizing compiler. We propose commensal compilation, a new strategy for compiling embedded domain-specific languages by reusing the massive investment in modern language virtual machine platforms. Commensal compilers use the host language's front-end, use host platform APIs that enable back-end optimizations by the host platform JIT, and use an autotuner for optimization selection. The cost of implementing a commensal compiler is only the cost of implementing the domain-specific optimizations. We demonstrate the concept by implementing a commensal compiler for the stream programming language StreamJIT atop the Java platform. Our compiler achieves performance 2.8 times better than the StreamIt native code (via GCC) compiler with considerably less implementation effort.

show abstract

XJava: Exploiting Parallelism with Object-Oriented Stream Programming

Cited by 9 publications

References 6 publications

StreamJIT

StreamJIT

Locating Parallelization Potential in Object-Oriented Data Structures

StreamJIT

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