PPT-Marco Salvi NVIDIA Deep Learning:

The Future of RealTime Rendering 1 Deep Learning is Changing the Way We Do Graphics Chaitanya17 Dahm17 Laine17 Holden17 Karras17 Nalbach17 Video AudioDriven Facial Animation by Joint EndtoEnd Learning of Pose and Emotion. Why NVIDIA GeForce Motherboard GPUs? NVIDIA NVIDIA Research. Anti-Aliasing: Are We There Yet?. 1. 2. “The Order: 1866” © Sony Computer Entertainment. 3. “Infiltrator” Unreal Engine 4 demo © Epic Games. 4. “Assassin’s Creed Unity” © . NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Retain performance. CUDA C/C++. Based on industry-standard C/C++. Small set of extensions to enable heterogeneous programming. CUDA Platform. CUDA Parallel Computing Platform. . Hardware . . . Capabilities. GPUDirect. SMX. Dynamic Parallelism. HyperQ. Programming . Approaches. Libraries. “Drop-in” Acceleration. Ang. . Li. University . of . Wisconsin-Madison. Outline. Brief Introduction of Background. Implementation. Evaluation. Conclusion. 3/20/2013. 2. NVIDIA GTC 2013. Background. JPEG Encoding. Parallelism Seeking. OpenACC. Directives. subroutine . saxpy. (n, a, x, y) . real :: x(:), y(:), a. integer :: n, . i. $!. acc. kernels. do . i. =1,n.   y(. i. ) = a*x(. i. )+y(. i. ). . enddo. $!. acc. end kernels. GDC 2011 NVIDIA GDC 2011 NVIDIA SPONSORED SESSIONS Samuel Gateau, NVIDIA Dominic Filion , Blizzard Stereoscopic 3D Demystified : From Theory to Implementation in Starcraft 2 GDC 2011 NVIDIA SPONSORE ECE 498AL, University of Illinois, Urbana-Champaign. 1. GPU Programming with CUDA . © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007-2009. ECE 498AL, University of Illinois, Urbana-Champaign. 2. A quiet revolution and potential build-up. Benchmark and Competition. Cody Coleman. ,. . Deepak . Narayanan, Daniel Kang, Tian . Zhao, Jian Zhang,. Luigi . Nardi. , Peter . Bailis. , . Kunle. . Olukotun. , Chris . Ré. , . Matei. . Zaharia. ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. heterogeneous programming. Brian Gregor. bgregor@bu.edu. Research Computing Services. Boston University. CUDA C/C BASICS. NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Retain performance. CUDA C/C . Based on industry-standard C/C . Small set of extensions to enable heterogeneous programming. A Tale of Two Cities: GPU Computing and Machine Learning Dr. Xiaowen Chu Department of Computer Science, Hong Kong Baptist University Outline 2 Some Stories of “Two Cities” Evolution of CPUs/ Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”.