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In this paper, MCL HPL model is modified to considering the memory access in calculation of communication overhead, and the enhanced MCL HPL performance modeler is implemented and tested. The proposed MCL HPL modeler is applied to estimate the performance of well-known TOP500 systems and compared the results to the original modeler and the real.
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HPL algorithms focus on developing and enhancing the basic linear algebra subprogram libraries for Linpack [, research on efficient block size determination [, communication overhead obfuscation during lower-upper (LU) factorization [, and fault tolerance [, efficient modifications and adaptations of HPL to general-purpose computing on graphics.
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An improved model for predicting HPL (High performance Linpack) performance is proposed by introducing a weighting function to account for many effects such that the proposed model could more accurately predict the maximal LINPACK performance Rmax. In this paper, we propose an improved model for predicting HPL (High performance Linpack) performance.
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model. The modeling of MCL HPL is described in Section 3. Sections 4 and 5 present some preliminary results and analy-sis for the proposed MCL HPL model and detailed application results for an experimental GPU cluster system, respectively. Finally, the conclusions and some additional considerations regarding the proposed MCL HPL model are.
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In this paper, we propose an improved model for predicting HPL (High performance Linpack) performance. In order to accurately predict the maximal LINPACK performance we first divide the performance model into two parts: computational cost and message passing overhead.
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High performance LINPACK (HPL) benchmark is used to evaluate the maximum floating-point performance of a computer cluster. Since the performance of the graphics processing unit (GPU) has been improved rapidly, many researchers start to optimize HPL benchmark through GPU to maximize system utilization.
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The roofline model also shows the balance point of each architecture between memory bandwidth and peak computational performance. Published in: 2022 International Conference on Electronics, Information, and Communication (ICEIC) Article #: Date of Conference: 06-09 February 2022 Date Added to IEEE Xplore: 11 April 2022 ISBN Information:
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HPL-AI: Mixed Precision Benchmark Is the same HPL benchmark but using lower/mixed precision that would more typically be used for training ML/AI models. On the A100 this utilizes TF32, 32-bit Tensor-Cores. This benchmark is now also part of the Top500 supercomputer rankings. HPCG: High Performance Conjugate Gradients, this is another benchmark.
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Abstract. In this paper, we propose an improved model for predicting HPL (High performance Linpack) performance. In order to accurately predict the maximal LINPACK performance we first divide the.