I/O Chunking and Latency Hiding Approach for Out-of-Core Sorting Acceleration Using GPU and Flash NVM
I/O Chunking and Latency Hiding Approach for Out-of-Core Sorting Acceleration Using GPU and Flash NVM
复制标题
使用 GPU 和 Flash NVM 进行核外排序加速的 I/O 分块和延迟隐藏方法
DOI：
10.1109/bigdata.2016.7840629
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Hirotaka Ogawa
中科院分区：
文献类型：
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作者：
Hitoshi Sato;Ryo Mizote;Satoshi Matsuoka;Hirotaka Ogawa
We propose an out-of-core sorting acceleration technique, called xtr2sort, that deals with multi-level memory hierarchies of device memory (GPU), host memory (CPU), and semi-external non-volatile memory (Flash NVM) for leveraging the high computational performance and memory bandwidth of GPUs, while offloading bandwidth-oblivious operations onto semi-external memory in order to significantly increasing the memory capacity available for the sort data, well beyond the that of the GPU as well as of the CPU. xtr2sort splits the input records into several chunks to fit in GPU device memory and overlaps (1) I/O operations between semi-external and host memory, (2) data transfers between host and device memory, and (3) sorting on the GPU device in an asynchronous manner for hiding latency. Experimental results show that xtr2sort can sort records up to 256 times larger than is possible with in-core GPU sorting and 16 times larger than is possible with in-core CPU sorting. xtr2sort also achieves 4.39 times faster than out-of-core CPU sorting using 72 threads on 204.8 giga records with int32_t, even though the input records could not fit in the host memory, let alone the GPU device memory. These results indicate that I/O chunking and latency hiding/overlapping maintains sorting performance, despite slow Flash NVM performance, by utilizing GPUs along with good algorithms. Such an approach is viable for accelerating future computing systems with deep memory hierarchies.
