A performance study of general-purpose applications on graphics processors using CUDA

By Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W. Sheaffer, Kevin Skadron. J. Parallel Distributed Comput., 2008

710 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/81c2a5fcdb3b192790d484ea822cce888b77f66b

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Citations in the corpus

Accelerating SQL database operations on a GPU with CUDA (2010, background): “The modern GPU s parallel architecture gives it very high throughput on certain problems, and its near universal use in desktop computers means that it is a cheap and ubiquitous source of processing power.”
Deep Learning for Audio Signal Processing (2019, methodology): “Instead, processors optimized for matrix operations are commonly used, mostly general-purpose graphics processing units (GPGPUs) [150] and application-specific integrated circuits such as the proprietary tensor processing units (TPUs) [8].”
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer (2011, background): “Numerous papers have demonstrated the impressive computing power of GPUs, and speedups of 1000x across a number of different types of applications have been reported over similar algorithms written for CPUs (for example Che et al. [1] and Ryoo et al. [2]).”
A quantitative study of irregular programs on GPUs (2012, background): “There exists a broad base of knowledge on the efficient parallelization of these algorithms [18], and their GPU implementations can be tens of times faster than tuned parallel CPU versions [10].”
Rodinia: A benchmark suite for heterogeneous computing (2009, methodology): “Inthis paper, we extend and characterize the Rodinia benchmark suite [4], a set of applications developed to address these concerns.”
High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster (2010, background): “Contents lists available at ScienceDirect Journal of Computational Physics journal homepage: www.elsevier .com/locate / jcp continues to improve at a rate substantially higher than CPU performance.”
Medical image processing on the GPU - Past, present and future (2013, methodology): “Chen et al. (2006) combined fast region growing and real-time visualization for interactive segmentation of brains.”
A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads (2010, background): “Low IPC can be attributed to any of myriad faults: there is limited parallelism per iteration in Needleman-Wunsch due to the dependencies of processing data elements in a diagonal strip manner [9]; the overhead of the GPU’s global memory accesses dominates BreadthFirst Search; and some applications ”
Kernel Fusion: An Effective Method for Better Power Efficiency on Multithreaded GPU (2010, methodology): “Through fusing two or more independent kernels, kernel fusion method achieves higher utilization and much more balanced demand for hardware resources, which provides much more potential for power optimization, such as dynamic voltage and frequency scaling (DVFS).”