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GTC ON-DEMAND

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Abstract:

Learn about the excitements and challenges in optimizing CUDA kernels for the last two generations of NVIDIA GPGPUs. Autotuning, although crucially important, is merely a silver bullet to port code from one generation of GPU to another. The process required many steps: (a) architecture specific algorithms, (b) tuning algorithms, (c) finding innovative tricks to handle generic cases, (d) tweaking GPU's internal scheduling to handle partition camping, and (e) above all, the dedication of many enthusiastic programmers. We will share our experiences and discoveries through the development of MAGMABLAS - a subset of CUDA BLAS, highly optimized for NVIDIA GPGPUs.

Learn about the excitements and challenges in optimizing CUDA kernels for the last two generations of NVIDIA GPGPUs. Autotuning, although crucially important, is merely a silver bullet to port code from one generation of GPU to another. The process required many steps: (a) architecture specific algorithms, (b) tuning algorithms, (c) finding innovative tricks to handle generic cases, (d) tweaking GPU's internal scheduling to handle partition camping, and (e) above all, the dedication of many enthusiastic programmers. We will share our experiences and discoveries through the development of MAGMABLAS - a subset of CUDA BLAS, highly optimized for NVIDIA GPGPUs.

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Topics:
Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2248
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