PPT-Warp-Level Divergence in GPUs:

Characterization Impact and Mitigation Ping Xiang Yi Yang Huiyang Zhou 1 The 20th IEEE International Symposium On High Performance Computer Architecture Orlando Florida . I was blindfolded. I was taken from my home. I was led to a dark room. Meet Olaf. A snowman who looks forward to summer. Specious reasoning, friend Olaf.  . The hot and the cold are both so intense. David Alan Paterson. 23 Aug 2011. Part 1. Infinite numbers as the limits of sequences of real numbers. Part 2. Oscillatory sequences & applications. Banishing divergence: Omega. Banishing Divergence: Big O notation. . Mauricio Hess-Flores. 1. , . Daniel Knoblauch. 2. , Mark . A. . Duchaineau. 3. , Kenneth . I. . Joy. 4. , . Falko. . Kuester. 5. Abstract. . - . An algorithm that shows how ray divergence in . multi-view stereo . Goal. Idle thread. Active thread. Compaction. Compact threads in a warp to coalesce (and eliminate) idle cycles .  improve utilization. References. V. . Narasiman. , et. al., “Improving GPU Performance via Large Warps and Two-Level Scheduling,” MICRO 2011. Instructor Notes. This lecture deals with how work groups are scheduled for execution on the compute units of devices. Also explain the effects of divergence of work items within a group and its negative effect on performance. to Improve GPGPU Performance. Rachata. . Ausavarungnirun. Saugata. . Ghose, . Onur. . Kayiran, Gabriel H. . Loh. . Chita . Das, . Mahmut. . Kandemir. , . Onur. . Mutlu. Overview of This Talk. Problem: . LO: to understand further issues about Language and Occupation. Starter: Look . at the examples below. In each case, try to explain what kind of language interaction is taking place and what form of utterance the speaker is . T. Rogers, M O’Conner, and T. . Aamodt. MICRO 2012. Goal. Understand the relationship between schedulers (warp/wavefront) and locality behaviors . Distinguish between inter-wavefront and intra-wavefront locality. Supercomputing. The Next wave of HPC. Presented by Shel Waggener. HP Materials from Marc Hamilton. June. , . 2011. © Copyright 2011 Hewlett-Packard Development Company, L.P.    . GPUs – changing the Economics of Supercomputing. EXTRA WARP/WEFT. Extra warp or weft (supplementary warp/weft).  . A class of weave in which extra warp of weft threads are used in addition to ground threads. The extra threads are normally used as figuring threads for decorative purposes.. T. Rogers, M O’Conner, and T. . Aamodt. MICRO 2012. Goal. Understand the relationship between schedulers (warp/wavefront) and locality behaviors . Distinguish between inter-wavefront and intra-wavefront locality. Value . Similarity . Daniel Wong. †. , Nam Sung Kim. ‡. , . Murali. . Annavaram. ¥. †. University of California, Riverside. dwong@ece.ucr.edu. ‡. University of Illinois, Urbana-. Champagin. Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. Profiling, AWS Cluster. Synchronization. Ideal case for parallelism: . no resources shared between threads. no communication between threads. . Many algorithms that require just a little bit of resource sharing can still be accelerated by massive parallelism of GPU.