Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing

Yang, Chi; Wang, Feng; Du, Yunfei; Chen, Juan; Liu, Jie; Yi, Huizhan; Lü, Kai

doi:10.1109/cluster.2010.12

Cited by 75 publications

(45 citation statements)

References 12 publications

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“…In [1] a dynamic task partition method was proposed. It divides parallel computing tasks according to execution speed to achieve best overall system performance.…”

Section: Related Researchesmentioning

confidence: 99%

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

Cao

Wang

2017

ITM Web Conf.

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Abstract. With the development of GPU general-purpose computing, GPU heterogeneous cluster has become a widely used parallel data processing solution in modern data center. Temperature management and controlling has become a new research hotspot in big data continuous computing. Temperature heat island in cluster has important influence on computing reliability and energy efficiency. In order to prevent the occurrence of GPU cluster temperature heat island, a big data task scheduling model for preventing temperature heat island was proposed. In this model, temperature, reliability and computing performance are taken into account to reduce node performance difference and improve throughput per unit time in cluster. Temperature heat islands caused by slow nodes are prevented by optimizing scheduling. The experimental results show that the proposed scheme can control node temperature and prevent the occurrence of temperature heat island under the premise of guaranteeing computing performance and reliability.

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“…In [1] a dynamic task partition method was proposed. It divides parallel computing tasks according to execution speed to achieve best overall system performance.…”

Section: Related Researchesmentioning

confidence: 99%

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

Cao

Wang

2017

ITM Web Conf.

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“…By using CPUs and accelerators together, hybrid computing can deliver higher performance than CPU-only or GPU-only execution [23]. Nevertheless, hybrid computing algorithms and systems do not typically take energy efficiency into account.…”

Section: Related Workmentioning

confidence: 99%

Performance and Energy Modeling for Cooperative Hybrid Computing

Feng

Burtscher

et al. 2014

2014 9th IEEE International Conference on Networking, Architecture, and Storage

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Abstract-Accelerator-based heterogeneous systems can provide high performance and energy efficiency, both of which are key design goals in high performance computing. To fully realize the potential of heterogeneous architectures, software must optimally exploit the hosts' and accelerators' processing and power-saving capabilities. Yet, previous studies mainly focus on using hosts and accelerators to boost application performance. Power-saving features to improve the energy efficiency of parallel programs, such as Dynamic Voltage and Frequency Scaling (DVFS), remain largely unexplored.Recognizing that energy efficiency is a different objective than performance and should therefore be independently pursued, we study how to judiciously distribute computation between hosts and accelerators for energy optimization. We further explore energy-saving scheduling in combination with computation distribution for even larger gains. Moreover, we present PEACH, an analytical model for Performance and Energy Aware Cooperative Hybrid computing. With just a few system-and application-dependent parameters, PEACH accurately captures the performance and energy impact of computation distribution and energy-saving scheduling to quickly identify the optimal coupled strategy for achieving the best performance or the lowest energy consumption. PEACH thus eliminates the need for extensive profiling and measurement. Experimental results from two GPU-accelerated heterogeneous systems show that PEACH predicts the performance and energy of the studied codes with less than 3% error and successfully identifies the optimal strategy for a given objective.

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“…Fatica has implemented the Linpack Benchmark for GPU clusters by splitting the trailing submatrix into a single GPU and one CPU socket statically [15]. Yang et al introduces an adaptive partitioning technique to split the trailing submatrix into a single GPU and individual CPU cores to implement Linpack [32]. Qilin is a generic programming system that can automatically map computations to GPUs and CPUs through off-line trainings [24].…”

Section: Related Workmentioning

confidence: 99%

Enabling and scaling matrix computations on heterogeneous multi-core and multi-GPU systems

Song

Tomov

Dongarra

2012

Proceedings of the 26th ACM International Conference on Supercomputing

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We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and multi-GPU systems to support dense matrix computations efficiently. The main idea is that we treat a heterogeneous system as a distributedmemory machine, and use a heterogeneous multi-level block cyclic distribution method to allocate data to the host and multiple GPUs to minimize communication. We design heterogeneous algorithms with hybrid tiles to accommodate the processor heterogeneity, and introduce an auto-tuning method to determine the hybrid tile sizes to attain both high performance and load balancing. We have also implemented a new runtime system and applied it to the Cholesky and QR factorizations. Our approach is designed for achieving four objectives: a high degree of parallelism, minimized synchronization, minimized communication, and load balancing. Our experiments on a compute node (with two Intel Westmere hexa-core CPUs and three Nvidia Fermi GPUs), as well as on up to 100 compute nodes on the Keeneland system [31], demonstrate great scalability, good load balancing, and efficiency of our approach.

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Adaptive Optimization for Petascale Heterogeneous CPU/GPU Computing

Cited by 75 publications

References 12 publications

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

A GPU Heterogeneous Cluster Scheduling Model for Preventing Temperature Heat Island

Performance and Energy Modeling for Cooperative Hybrid Computing

Enabling and scaling matrix computations on heterogeneous multi-core and multi-GPU systems

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