[Harvard CS264] 11a - Programming the Memory Hierarchy with Sequoia (Mike Bauer, Stanford)

[Harvard CS264] 11b - Analysis-Driven Performance Optimization with CUDA (Cliff Woolley, NVIDIA)

[Harvard CS264] 15a - The Onset of Parallelism, Changes in Computer Architecture and Microsoft's Role in the Transition (David Rich, Microsoft Research)

[Harvard CS264] 12 - Irregular Parallelism on the GPU: Algorithms and Data Structures (John Owens, UC Davis)

[Harvard CS264] 01 - Introduction

MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)

IAP09 CUDA@MIT 6.963 - Lecture 04: CUDA Advanced #1 (Nicolas Pinto, MIT)

IAP09 CUDA@MIT 6.963 - Lecture 07: CUDA Advanced #2 (Nicolas Pinto, MIT)

IAP09 CUDA@MIT 6.963 - Guest Lecture: CUDA Tricks and High-Performance Computational Physics (Kipton Barros, BU)

IAP09 CUDA@MIT 6.963 - Lecture 05: Wheel Of Reincarnation (Steven Johnson, MIT)

IAP09 CUDA@MIT 6.963 - Guest Lecture: Out-of-Core Programming with NVIDIA's CUDA (Gene Cooperman, NEU)

[Harvard CS264] 14 - Dynamic Compilation for Massively Parallel Processors (Gregory Diamos, Georgia Tech)

[Harvard CS264] 15a - Jacket: Visual Computing (James Malcolm, Accelereyes)

[Harvard CS264] 13 - The R-Stream High-Level Program Transformation Tool / Programming GPUs without Writing a Line of CUDA (Nicolas Vasilache, Reservoir Labs)

[Harvard CS264] 10b - cl.oquence: High-Level Language Abstractions for Low-Level Programming (Cyrus Omar, CMU)

[Harvard CS264] 05 - Advanced-level CUDA Programming

[Harvard CS264] 04 - Intermediate-level CUDA Programming

IAP09 CUDA@MIT 6.963 - Lecture 01: High-Throughput Scientific Computing (Hanspeter Pfister, Harvard)

[Harvard CS264] 16 - Managing Dynamic Parallelism on GPUs: A Case Study of High Performance Sorting (Duane Merrill, University of Virginia)

[Harvard CS264] 09 - Machine Learning on Big Data: Lessons Learned from Google Projects (Max Lin, Google Research)