2014 IEEE 28th International Parallel and Distributed Processing Symposium 2014
DOI: 10.1109/ipdps.2014.89
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BigKernel -- High Performance CPU-GPU Communication Pipelining for Big Data-Style Applications

Abstract: GPUs offer an order of magnitude higher compute power and memory bandwidth than CPUs. GPUs therefore might appear to be well suited to accelerate computations that operate on voluminous data sets in independent ways; e.g., for transformations, filtering, aggregation, partitioning or other "Big Data" style processing. Yet experience indicates that it is difficult, and often error-prone, to write GPGPU programs which efficiently process data that does not fit in GPU memory, partly because of the intricacies of G… Show more

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Cited by 22 publications
(12 citation statements)
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“…Finally, the high bandwidth of GPU memory can only be exploited when GPU threads executing at the same time access memory in a coalesced fashion, where the threads simultaneously access adjacent memory locations. For efficient streaming data filtering, we applied "BigKernel" [17] that is a data communication scheme between CPU and GPU to address the above issues. BigKernel can use a four-stage pipeline with an automated prefetching method to (i) optimize CPU-GPU communication and (ii) optimize GPU memory accesses.…”
Section: A Communication Scheme For Processing Geo-textual Streaming mentioning
confidence: 99%
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“…Finally, the high bandwidth of GPU memory can only be exploited when GPU threads executing at the same time access memory in a coalesced fashion, where the threads simultaneously access adjacent memory locations. For efficient streaming data filtering, we applied "BigKernel" [17] that is a data communication scheme between CPU and GPU to address the above issues. BigKernel can use a four-stage pipeline with an automated prefetching method to (i) optimize CPU-GPU communication and (ii) optimize GPU memory accesses.…”
Section: A Communication Scheme For Processing Geo-textual Streaming mentioning
confidence: 99%
“…We further employed a data streaming communication method [17] to optimize I/O overheads between GPU and CPU during continuously processing geo-textual streaming data.…”
mentioning
confidence: 99%
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“…Hence, its speedup can be a hundredfold or zero. Several ways to predict performance of OpenCL kernels for different devices have been mentioned in three extensive surveys (Mokhtari and Stumm, 2014;Rossbach et al, 2013;Yan et al, 2009). Kernel profiling is a key technique used to get information about kernels to be classified, e.g.…”
Section: Introductionmentioning
confidence: 99%
“…A CPU code for K-means clustering.2.4.3.3 Mokhtari et alBigKernel[92], a compiler and runtime technique to address several challenges associated with data processing involving GPGPU. The BigKernel also addresses the problem of uncoalesced memory accesses occurring in Big Data-style computations.…”
mentioning
confidence: 99%