Another view on parallel speedup

Sun, Dong; Ni$^{, }$

doi:10.1109/superc.1990.130037

Cited by 63 publications

(47 citation statements)

References 13 publications

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“…Because if there is no computation (i.e. application has pure I/O), no overlap Without KNOWAC prefetching With KNOWAC prefetching The scalability is tested based on the fixed-size scalability [33]. The number of I/O nodes is increased, but the input data remain the same.…”

Section: B Execution Time Improvementmentioning

confidence: 99%

KNOWAC: I/O Prefetch via Accumulated Knowledge

Sun

Thakur

2012

2012 IEEE International Conference on Cluster Computing

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Abstract-The lasting memory-wall problem combined with the newly emerged big-data problem makes data access delay the first citizen of performance optimizations of cluster computing. Reduction of data access delay, however, is application dependent. It depends on the data access behaviors of the underlying applications. Therefore, leaning and understanding data access behaviors is a must for effective data access optimizations. Modern microprocessors are equipped with hardware data prefetchers, which predict data access patterns and prefetch data for CPU. However, memory systems in design do not have the capability to understand data access behaviors for performance optimizations. In this study, we propose a novel approach, named KNOWAC, to collect I/O information automatically through highlevel I/O libraries. KNOWAC accumulates I/O knowledge and reveals data usage patterns by exploring the collected highlevel I/O characteristics. The discovered data usage patterns can be used for different I/O optimizations. We apply KNOWAC to I/O prefetch under the framework of PnetCDF in this study. Experimental results on a real-world application show that KNOWAC is promising and has a true practical value in mitigating the I/O bottleneck.

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Section: B Execution Time Improvementmentioning

confidence: 99%

KNOWAC: I/O Prefetch via Accumulated Knowledge

Sun

Thakur

2012

2012 IEEE International Conference on Cluster Computing

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“…Figure 3, based on [17], illustrates the differences between Amdahl's and Gustafson's law. Amdahl assumes that the amount of work that can be parallelized, Wp, is constant and independent of the number of cores p. This can be considered overly pessimistic.…”

Section: Amdahl's and Related Lawsmentioning

confidence: 99%

Amdahl's law for predicting the future of multicores considered harmful

Juurlink¹,

Meenderinck²

2012

SIGARCH Comput. Archit. News

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Several recent works predict the future of multicore systems or identify scalability bottlenecks based on Amdahl's law. Amdahl's law implicitly assumes, however, that the problem size stays constant, but in most cases more cores are used to solve larger and more complex problems. There is a related law known as Gustafson's law which assumes that runtime, not the problem size, is constant. In other words, it is assumed that the runtime on p cores is the same as the runtime on 1 core and that the parallel part of an application scales linearly with the number of cores. We apply Gustafson's law to symmetric, asymmetric, and dynamic multicores and show that this leads to fundamentally different results than when Amdahl's law is applied. We also generalize Amdahl's and Gustafson's law and study how this quantitatively effects the dimensioning of future multicore systems.

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“…It has been carefully examined in [4]. Average parallelism is equivalent to the maximum speedup S" [4,15]. S',…”

Section: Degree Of Parallelismmentioning

confidence: 99%

“…has been carefully studied in [15]. The structure of that study can be used as a guideline for other algorithms.…”

Section: Global Computationmentioning

confidence: 99%

Scalable Problems and Memory-Bounded Speedup

Sun

1993

Journal of Parallel and Distributed Computing

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130

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In this paper three models of parallel speedup are studied. They are fixed-size speedup, fixed-time speedup and memory-bounded speedup. The latter two consider the relationship between speedup and problem scalability. Two sets of speedup formulations are derived for these three models. One set considers uneven workload allocation and communication overhead, and gives more accurate estimation. Another set considers a simplified case and provides a clear picture on the impact of the sequential portion of an application on the possible performance gain from parallel processing. The simplified fixed-size speedup is Amdahl's law. The simplified fixed-time speedup is Gustafson's scaled speedup. The simplified memory-bounded speedup contains both Amdahl's law and Gustafson's scaled speedup as special cases. This study leads to a better understanding of parallel processing.

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Another view on parallel speedup

Cited by 63 publications

References 13 publications

KNOWAC: I/O Prefetch via Accumulated Knowledge

KNOWAC: I/O Prefetch via Accumulated Knowledge

Amdahl's law for predicting the future of multicores considered harmful

Scalable Problems and Memory-Bounded Speedup

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