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首页> 外文会议>2012 SC Companion: High Performance Computing, Networking, Storage and Analysis. >Poster: A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks
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Poster: A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks

机译：海报：基于精细粒度内存感知任务的新型混合CPU-GPU广义特征求解器，用于电子结构计算

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摘要

The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium-sized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to scale on distributed systems, but can benefit from the massive compute performance concentrated on a single node, hybrid GPU-CPU system. However, hybrid systems call for the development of new algorithms that efficiently exploit heterogeneity and massive parallelism of not just GPUs, but of multi/many-core CPUs as well. Addressing these demands, we developed a novel algorithm featuring innovative: Fine grained memory aware tasks; Hybrid execution/scheduling, and Increased computational intensity. The resulting eigensolvers are state-of-the-art in HPC, significantly outperforming existing libraries. We describe the algorithm and analyze its performance impact on applications of interest when different fractions of eigenvectors are needed by the host electronic structure code.
机译：在传统的超级计算平台（例如Cray-XK6）中采用混合GPU-CPU节点，为材料科学和化学应用中的电子结构计算提供了加速机会，在该应用中，中型广义特征值问题必须解决很多次。这些特征值问题太小，无法在分布式系统上扩展，但可以受益于集中在单节点混合GPU-CPU系统上的海量计算性能。但是，混合系统要求开发新算法，这些算法不仅可以有效利用GPU的异构性和大规模并行性，而且还可以利用多核/多核CPU。为了满足这些需求，我们开发了一种具有创新性的新颖算法：精细粒度的内存感知任务；混合执行/计划，并增加了计算强度。最终的特征求解器在HPC中是最先进的，大大优于现有的库。当主机电子结构代码需要不同比例的特征向量时，我们描述该算法并分析其性能对感兴趣的应用程序的影响。

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