Array optimizations for parallel implementations of high productivity languages

Joyner, Mackale; Budimlić, Zoran; Sarkar, Vivek; Zhang, Rui

doi:10.1109/ipdps.2008.4536185

Cited by 8 publications

(4 citation statements)

References 21 publications

(17 reference statements)

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“…Unlike a lot of past work on array distributions, the HPT approach to array distribution builds on the idea of array views [49]. In this approach, a base one-dimensional array can be allocated across a set of places, and then viewed through a multidimensional index space.…”

Section: Case Studymentioning

confidence: 99%

SLAW: A scalable locality-aware adaptive work-stealing scheduler

Zhao

Cavé

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing (IPDPS)

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Recent trend has made it clear that the processor makers are committed to the multicore chip designs. The number of cores per chip is increasing, while there is little or no increase in the clock speed. This parallelism trend poses a significant and urgent challenge on computer software because programs have to be written or transformed into a multi-threaded form to take full advantage of future hardware advances.Task parallelism has been identified as one of the prerequisites for software productivity. In task parallelism, programmers focus on decomposing the problem into subcomputations that can run in parallel and leave the compiler and runtime to handle the scheduling details. This separation of concerns between task decomposition and scheduling provides productivity to the programmer but poses challenges to the runtime scheduler.Our thesis is that work-stealing schedulers with adaptive scheduling policies and locality-awareness can provide a scalable and robust runtime foundation for multicore task parallelism. We evaluate our thesis using the new Scalable Locality-aware Adaptive Work-stealing (SLAW) runtime scheduler developed for the Habanero-Java programming language, a task-parallel variant of Java. SLAW's adaptive task scheduling is motivated by the study of two common scheduling policies in a work-stealing scheduler, specifically, the work-first and the help-first policy. Both policies exhibit limitations in performance and resource usage in different situations. The variances make it hard to determine the best policy a priori. SLAW addresses these limitations by supporting both policies simultaneously and selecting policies adaptively on a per-task basis at runtime. Our results show that SLAW achieves 0.98× to 9.2× speedup over the help-first scheduler and 0.97× to 4.5× speedup over the work-first scheduler. Further, for large irregular parallel computations, SLAW supports data sizes and achieves performance that cannot be delivered by the use of any single fixed policy.SLAW's locality-aware scheduling framework aims to overcome the cache unfriendliness of work-stealing due to randomized stealing. The SLAW scheduler is designed for programming models where locality hints are provided to the runtime by the programmer or compiler. Our results show that locality-aware scheduling can improve performance by increasing temporal data reuse for iterative data-parallel applications.iii

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Section: Case Studymentioning

confidence: 99%

SLAW: A scalable locality-aware adaptive work-stealing scheduler

Zhao

Cavé

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing (IPDPS)

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“…Distributions of arrays can be more complicated, as evidenced by the wealth of past work on this topic including array distributions in High Performance Fortran. Unlike a lot of past work on array distributions, the HPT approach to array distribution builds on the idea of array views [13,12]. In this approach, a base one-dimensional array can be allocated across a set of places, and then viewed through a multidimensional index space.…”

Section: Implicit Data Transfer With Data Distributions and Array Viewsmentioning

confidence: 99%

Hierarchical Place Trees: A Portable Abstraction for Task Parallelism and Data Movement

Yan

Zhao

et al. 2010

Languages and Compilers for Parallel Computing

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Modern computer systems feature multiple homogeneous or heterogeneous computing units with deep memory hierarchies, and expect a high degree of thread-level parallelism from the software. Exploitation of data locality is critical to achieving scalable parallelism, but adds a significant dimension of complexity to performance optimization of parallel programs. This is especially true for programming models where locality is implicit and opaque to programmers. In this paper, we introduce the hierarchical place tree (HPT) model as a portable abstraction for task parallelism and data movement. The HPT model supports co-allocation of data and computation at multiple levels of a memory hierarchy. It can be viewed as a generalization of concepts from the Sequoia and X10 programming models, resulting in capabilities that are not supported by either. Compared to Sequoia, HPT supports three kinds of data movement in a memory hierarchy rather than just explicit data transfer between adjacent levels, as well as dynamic task scheduling rather than static task assignment. Compared to X10, HPT provides a hierarchical notion of places for both computation and data mapping. We describe our work-in-progress on implementing the HPT model in the Habanero-Java (HJ) compiler and runtime system. Preliminary results on general-purpose multicore processors and GPU accelerators indicate that the HPT model can be a promising portable abstraction for future multicore processors.

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“…On the other hand, maximum utilization of powerful hardware usually requires low-level programming. Numerous parallel languages [11] are emerging to resolve the contradiction but each of them has their own limitation. 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.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Smith-Waterman sequence database search in X10

Liu

Yang

2012

Proceedings of the 2012 ACM SIGPLAN X10 Workshop

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Productivity and performance are always viewed as two sides of parallel programming languages. X10 is a new object-oriented parallel language for both high-productivity and high-performance. To help the development of X10, we characterize the performance of X10 in bioinformatics using the fundamental application SmithWaterman (SW) sequence database search. We implement the SW application in X10 on multi-core shared-memory architecture. Through comparing with three SW implementations in C++, we make following suggestions for X10 as well as its compiler.(1) X10 compiler should improve its array access implementation in kernel loop to avoid redundant check and inefficient offset computation. The array access of the latest version X10 is much slower than that of C++, which results in poor singlecore performance of SW in X10.(2) X10 should support the utilization of SIMD instructions. With 128-bit SSE instructions, SW in X10 can achieve 8.7-17.7 fold speedup. Note that there are many applications in the world which can dramatically benefit from SIMD architectures on modern processors.

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Array optimizations for parallel implementations of high productivity languages

Cited by 8 publications

References 21 publications

SLAW: A scalable locality-aware adaptive work-stealing scheduler

SLAW: A scalable locality-aware adaptive work-stealing scheduler

Hierarchical Place Trees: A Portable Abstraction for Task Parallelism and Data Movement

Characterization of Smith-Waterman sequence database search in X10

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