A pilot study to compare programming effort for two parallel programming models

Hochstein, Lorin; Basili, Victor R.; Vishkin, Uzi; Gilbert, John R.

doi:10.1016/j.jss.2007.12.798

Cited by 37 publications

(23 citation statements)

References 25 publications

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“…The group around Hochstein, Basili, and Carver conducted multi-institutional experiments [4], [3] in the area of high performance computing using parallel programming assignments and students as subjects. In all these experiments, time to completion is the main measure taken.…”

Section: Related Workmentioning

confidence: 99%

Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages

Nanz

Torshizi

Pedroni

et al. 2011

2011 International Symposium on Empirical Software Engineering and Measurement

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The recent turn towards multicore processing architectures has made concurrency an important part of mainstream software development. As a result, an increasing number of developers have to learn to write concurrent programs, a task that is known to be hard even for the expert. Language designers are therefore working on languages that promise to make concurrent programming "easier". However, the claim that a new language is more usable than another cannot be supported by purely theoretical considerations, but calls for empirical studies. In this paper, we present the design of a study to compare concurrent programming languages with respect to comprehending and debugging existing programs and writing correct new programs. A critical challenge for such a study is avoiding the bias that might be introduced during the training phase and when interpreting participants' solutions. We address these issues by the use of self-study material and an evaluation scheme that exposes any subjective decisions of the corrector, or eliminates them altogether. We apply our design to a comparison of two object-oriented languages for concurrency, multithreaded Java and SCOOP (Simple Concurrent ObjectOriented Programming), in an academic setting. We obtain results in favor of SCOOP even though the study participants had previous training in writing multithreaded Java programs.

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Section: Related Workmentioning

confidence: 99%

Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages

Nanz

Torshizi

Pedroni

et al. 2011

2011 International Symposium on Empirical Software Engineering and Measurement

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“…-the ease of teaching of XMT programming as an adoption benchmark has been established in repeated instances, from middle-school and up, and by independent education experts [29], and shown to be superior to alternative parallel approaches such as MPI, OpenMP and CUDA; -XMT is based on a rich algorithmic theory (PRAM) that provides a solid framework for designing and analyzing algorithms, equivalent to the serial model; -XMT provides a programmer's workflow for deriving efficient programs from PRAM algorithms, and reasoning about their execution time [32] and correctness; -in a semester-long study supported through the DARPA HPCS program, the development time of XMT was, not surprisingly, shown to be about half that of MPI under circumstances favoring MPI [11].…”

Section: Global Register Filementioning

confidence: 99%

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

Caragea

Tzannes

Keceli

et al. 2011

Int J Parallel Prog

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Abstract. Super-scalar, out-of-order processors that can have tens of read and write requests in the execution window place significant demands on Memory Level Parallelism (MLP). Multi-and many-cores with shared parallel caches further increase MLP demand. Current cache hierarchies however have been unable to keep up with this trend, with modern designs allowing only 4-16 concurrent cache misses. This disconnect is exacerbated by recent highly parallel architectures (e.g. GPUs) where power and area per-core budget favor numerous lighter cores with less resources, further reducing support for MLP on a per-core basis. Support for hardware and software prefetch increases MLP pressure since these techniques overlap multiple memory requests with existing computation. In this paper, we propose and evaluate a novel Resource-Aware Prefetching (RAP) compiler algorithm that is aware of the number of simultaneous prefetches supported, and optimized for the same. We implemented our algorithm in a GCC-derived compiler and evaluated its performance using an emerging fine-grained many-core architecture. Our results show that the RAP algorithm outperforms a well-known loop prefetching algorithm by up to 40.15% in run-time on average across benchmarks and the state-of-the art GCC implementation by up to 34.79%, depending upon hardware configuration. Moreover, we compare the RAP algorithm with a simple hardware prefetching mechanism, and show run-time improvements of up to 24.61%. To demonstrate the robustness of our approach, we conduct a designspace exploration (DSE) for the considered target architecture by varying (i) the amount of chip resources designated for per-core prefetch storage and (ii) off-chip bandwidth. We show that the RAP algorithm is robust in that it improves performance across all design points considered. We also identify the Pareto-optimal hardware-software configuration which delivers 53.66% run-time improvement on average while using only 5.47% more chip area than the bare-bones design.

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“…Indications that XMT is an easy-toprogram efficient parallel architecture, include: (i) XMT is based on a rich algorithmic theory (PRAM) that provides a solid framework for designing and analyzing algorithms, equivalent to the serial model; (ii) the ease of teaching of XMT programming as an adoption benchmark has been established in repeated instances, from middle-school and up, and by independent education experts [9], and shown to be superior to alternative parallel approaches such as MPI, OpenMP and CUDA; (iii) XMT provides a programmer's workflow for deriving efficient programs from PRAM algorithms, and reasoning about their execution time [10] and correctness, and (iv) in a semester-long study supported through the DARPA HPCS program, the development time of XMT was, not surprisingly, shown to be about half that of MPI under circumstances favoring MPI [11].…”

Section: The Xmt Frameworkmentioning

confidence: 99%

Resource-Aware Compiler Prefetching for Many-Cores

Caragea

Tzannes

Keceli

et al. 2010

2010 Ninth International Symposium on Parallel and Distributed Computing

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Abstract-Super-scalar, out-of-order processors that can have tens of read and write requests in the execution window place significant demands on Memory Level Parallelism (MLP). Multi-and many-cores with shared parallel caches further increase MLP demand. Current cache hierarchies however have been unable to keep up with this trend, with modern designs allowing only 4-16 concurrent cache misses. This disconnect is exacerbated by recent highly parallel architectures (e.g. GPUs) where power and area per-core budget favor lighter cores with less resources.Support for hardware and software prefetch increase MLP pressure since these techniques overlap multiple memory requests with existing computation. In this paper, we propose and evaluate a novel Resource-Aware Prefetching (RAP) compiler algorithm that is aware of the number of simultaneous prefetches supported, and optimized for the same. We show that in situations where not enough resources are available to issue prefetch instructions for all references in a loop, it is more beneficial to decrease the prefetch distance and prefetch for as many references as possible, rather than use a fixed prefetched distance and skip prefetching for some references, as in current approaches.We implemented our algorithm in a GCC-derived compiler and evaluated its performance using an emerging fine-grained many-core architecture. Our results show that the RAP algorithm outperforms a well-known loop prefetching algorithm by up to 40.15% and the state-of-the art GCC implementation by up to 34.79%. Moreover, we compare the RAP algorithm with a simple hardware prefetching mechanism, and show improvements of up to 24.61%.

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A pilot study to compare programming effort for two parallel programming models

Cited by 37 publications

References 25 publications

Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages

Design of an Empirical Study for Comparing the Usability of Concurrent Programming Languages

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

Resource-Aware Compiler Prefetching for Many-Cores

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