GPU support for batch oriented workloads
GPU support for batch oriented workloads
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DOI：
10.1109/pccc.2009.5403809
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
L. Costa;S. Al-Kiswany;M. Ripeanu
中科院分区：
文献类型：
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作者：
L. Costa;S. Al-Kiswany;M. Ripeanu
本文探讨了使用图形处理单元（GPU）作为协处理器来利用高性能系统中批处理操作的固有并行性的能力。为此，我们选择了布隆过滤器（支持集合成员的概率表示的空间高效的数据结构），因为这些数据结构支持的查询通常是批量执行的。布隆过滤器显示出单位数据量的低计算成本，为更复杂的批处理操作提供了基准。我们实现了BloomGPU库，支持卸载布隆过滤器支持GPU和评估这个库下的现实使用场景。通过将布隆过滤器操作完全卸载到GPU，随着工作负载变得更大，BloomGPU的性能优于布隆过滤器的优化CPU实现。
This paper explores the ability to use Graphics Processing Units (GPUs) as co-processors to harness the inherent parallelism of batch operations in systems that require high performance. To this end we have chosen Bloom filters (space-efficient data structures that support the probabilistic representation of set membership) as the queries these data structures support are often performed in batches. Bloom filters exhibit low computational cost per amount of data, providing a baseline for more complex batch operations. We implemented BloomGPU a library that supports offloading Bloom filter support to the GPU and evaluate this library under realistic usage scenarios. By completely offloading Bloom filter operations to the GPU, BloomGPU outperforms an optimized CPU implementation of the Bloom filter as the workload becomes larger.