Algorithms for clustering data
Algorithms for clustering data
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
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With increasing need for accelerating data mining and scientific data analysis on large data sets, and less chance to improve processor performance by simply increasing clock frequencies, multi-core architectures and accelerators like FPGAs and GPUs have become popular. A recent development in using GPU for general computing has been the release of CUDA (Compute Unified Device Architecture) by NVIDIA. CUDA allows GPU programming with Clanguage-like features, thus easing the development of non-graphics applications on a GPU. However, several challenges still remain in programming the GPUs with CUDA, because CUDA involves explicit parallel programming and management of its complex memory hierarchy, as well as allocating device memory, moving data between CPU anddevice memory, and specification of thread grid configurations. In this paper, we offer a solution for the programmers to generate CUDA code by specifying the sequential reduction loop(s) with some information about the parameters. With program analysis and code generation, the applications are mapped to a GPU. Several additional optimizations are also performed by the middleware. We have evaluated our system using three popular data miningapplications, k-means clustering, EM clustering, and Principal Component Analysis (PCA). The speedup that each of these applications achieve over a sequential CPU version ranges between 20 and 50.