PPT-Warp Speed: Executing Time Warp on 1,966,080 Cores

Chris Carothers Justin LaPre RPI chrisc laprejcsrpiedu Peter Barnes David Jefferson LLNL barnes26 jefferson6llnlgov Outline Motivation Blue GeneQ CCNI LLNLs Sequoia ROSS Implementation. Wearable Reference Platform (WaRP) with Standard Daughter Board i.MX 6SoloLiteBased on ARM Ibrahim, G., E., & . Dorgham. , M.,E. .,. SCIENCES &ARTS-RESEARCH STUDIES VOL.23 NO.2 APRIL 2011 . Abstract. . The aim of this research is to follow up the production lines of warp threads preparations and to find the errors and defects always associated with this phase and identify the causes and quantify and try to minimize them and reduce their appearance and get rid of the obstacles affecting the quality of this stage and treat them which will enhance and not disrupt the following stages in the production process.. 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. to Improve GPGPU Performance. Rachata. . Ausavarungnirun. Saugata. . Ghose, . Onur. . Kayiran, Gabriel H. . Loh. . Chita . Das, . Mahmut. . Kandemir. , . Onur. . Mutlu. Overview of This Talk. Problem: . Graphics Processing Unit (GPU). GPU is the . chip . in computer video cards, PS3, Xbox, . etc. Designed to realize the 3D graphics pipeline. Application .  Geometry  Rasterizer image. GPU development:. 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. 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.. Loose Round Robin (LRR). Goes around to every warp . and issue if ready (R). If warp is not ready (W), . skip and issue next ready warp. Issue: Warps all run at the same speed,. potentially all reaching memory access. Why is GPU in the picture. Seeking . exa. -scale computing platform. Minimize . power per operation.. Power is directly correlated to the area in the processing chips.. Regular CPU. . – what are the chip areas going into?. ©Chad Kersey and Sudhakar Yalamanchili unless otherwise noted. Objectives. Detailed look at the implementation of a SIMT GPU. Example of the type of information propagated down the pipeline. Basis for the next assignment and the default project. Value . Similarity . Daniel Wong. †. , Nam Sung Kim. ‡. , . Murali. . Annavaram. ¥. †. University of California, Riverside. dwong@ece.ucr.edu. ‡. University of Illinois, Urbana-. Champagin. Characterization. , . Impact. , and . Mitigation. Ping Xiang. ,. . Yi Yang,. . Huiyang. . Zhou. 1. The 20th IEEE International Symposium On High Performance Computer Architecture. , Orlando, Florida, . Goes around to every warp . and issue if ready (R). If warp is not ready (W), . skip and issue next ready warp. Issue: Warps all run at the same speed,. potentially all reaching memory access. phase together and stalling.. Chris Hall. BLAST Workshop - May 7 - 9, 2018. 1. Challenges in Computational Science. BLAST Workshop - May 7 - 9, 2018. 2. Code installation and maintenance. Different architectures/operating systems.