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"AI" for Blockchain Security (Case Study: Cosmos)
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High-Performance Computing Needs Machine Learning... And Vice Versa (NIPS 2011, Big Learning)
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[Harvard CS264] 16 - Managing Dynamic Parallelism on GPUs: A Case Study of High Performance Sorting (Duane Merrill, University of Virginia)
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[Harvard CS264] 15a - The Onset of Parallelism, Changes in Computer Architecture and Microsoft's Role in the Transition (David Rich, Microsoft Research)
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[Harvard CS264] 15a - Jacket: Visual Computing (James Malcolm, Accelereyes)
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[Harvard CS264] 14 - Dynamic Compilation for Massively Parallel Processors (Gregory Diamos, Georgia Tech)
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[Harvard CS264] 13 - The R-Stream High-Level Program Transformation Tool / Programming GPUs without Writing a Line of CUDA (Nicolas Vasilache, Reservoir Labs)
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[Harvard CS264] 12 - Irregular Parallelism on the GPU: Algorithms and Data Structures (John Owens, UC Davis)
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[Harvard CS264] 11b - Analysis-Driven Performance Optimization with CUDA (Cliff Woolley, NVIDIA)
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[Harvard CS264] 11a - Programming the Memory Hierarchy with Sequoia (Mike Bauer, Stanford)
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[Harvard CS264] 10b - cl.oquence: High-Level Language Abstractions for Low-Level Programming (Cyrus Omar, CMU)
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[Harvard CS264] 10a - Easy, Effective, Efficient: GPU Programming in Python with PyOpenCL and PyCUDA (Andreas Kloeckner, NYU)
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[Harvard CS264] 09 - Machine Learning on Big Data: Lessons Learned from Google Projects (Max Lin, Google Research)
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[Harvard CS264] 08a - Cloud Computing, Amazon EC2, MIT StarCluster (Justin Riley, MIT)
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[Harvard CS264] 08b - MapReduce and Hadoop (Zak Stone, Harvard)
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