PPT-OpenCL

HighLevel Synthesis for Mainstream FPGA Acceleration James Coole PhD student University of Florida Dr Greg Stitt Associate Professor of ECE University of Florida SHAW Workshop This work is supported by National Science Foundation grant CNS1149285 and the IUCRC Program of the National Science Foundation under Grant No EEC0642422. Robert . Senser. October 29, 2014. 1. GPU DECLARATIVE . FRAMEWORK:. DEFG. PhD Committee:. Gita . Alaghband. (chair). Tom . Altman (advisor). Michael . Mannino. Boris . Stilman. . Tam . Vu. Presentation Outline. Instructor Notes. Discusses synchronization, timing and profiling in OpenCL. Coarse grain synchronization covered which discusses synchronizing on a command queue granularity. Discuss different types of command queues (in order and out of order) . Processing . on OpenCL-based FPGAs. Zeke Wang. , Johns Paul, Hui Yan Cheah . (. NTU,. . Singapore), . Bingsheng He (. NUS,. Singapore), . Wei Zhang (HKUST, Hong Kong). 1. Outline. Background and Problem. James . Coole. PhD student, University of . Florida. Aaron . Landy. PhD student, University of Florida. Greg . Stitt. Associate . Professor of ECE, University of Florida. Catapult Workshop. This work is supported by National Science Foundation grant CNS-1149285 and the I/UCRC Program of the National Science Foundation under Grant No. EEC-0642422.. James . Coole. PhD student, University of . Florida. Aaron . Landy. PhD student, University of Florida. Greg . Stitt. Associate . Professor of ECE, University of Florida. Catapult Workshop. This work is supported by National Science Foundation grant CNS-1149285 and the I/UCRC Program of the National Science Foundation under Grant No. EEC-0642422.. Instructor Notes. This is a brief lecture which goes into some more details on OpenCL memory objects. Describes various flags that can be used to change how data is handled between host and device, like page-locked I/O and so on. Instructor Notes. This lecture describes the different ways to work with multiple devices in . OpenCL. (i.e., within a single context and using multiple contexts), and the tradeoffs associated with each approach. 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. Phitchaya. . Mangpo. . Phothilimthana. Jason . Ansel. Jonathan . Ragan-Kelley. Saman. . Amarasinghe. Computer Science and Artificial Intelligence Laboratory. Massachusetts Institute of Technology. K. ainz. Overview. About myself. Motivation. GPU hardware and system architecture. GPU programming languages. GPU programming paradigms. Pitfalls and best practice. Reduction and tiling examples. State-of-the-art . Bojie Li 1,2 , Kun Tan 1 , Layong (Larry) Luo 1 , Yanqing Peng 1,3 , Renqian Luo 1,2 , Ningyi Xu 1 , Yongqiang Xiong 1 , Peng Cheng 1 , Enhong Chen 2 1 Microsoft Research, 2 USTC, 3 SJTU Implementing Waters. Introduction to GPU Computing. Brief History of GPU Computing. Technical Issues. Social Impact. Marketing and Ethical . Issues. Project Management. Conclusion. Table of Contents. A . GPU is . kihívások és lehetőségek. *. embarassingly. parallel. Hatékony, alacsony-szintű absztrakció. Nyílt platform soha nem látott változatosságú hardverek programozására (CPU, GPU, APU, . accelerator. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand