PPT-Warp Scheduling

Author : mitsue-stanley | Published Date : 2017-09-13

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

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Warp Scheduling: Transcript

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. Wearable Reference Platform (WaRP) with Standard Daughter Board i.MX 6SoloLiteBased on ARM High Performance Computing . for Engineering Applications. “Computers are useless. They can only give you . answers.” . Pablo Picasso. © Dan Negrut, . 2011. ME964 . UW-Madison. Execution Scheduling in CUDA. : Speeding up GPU Warps . by . Reducing Memory . Pitstops. Ankit Sethia* D. . Anoushe. Scott Mahlke. . Jamshidi. University of Michigan. Graphics. Simulation . Linear Algebra. Data Analytics. 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: . Adwait Jog. , Onur Kayiran, Asit Mishra, Mahmut Kandemir, Onur Mutlu, Ravi Iyer, Chita Das . Multi-threading. Caching. Prefetching . Main Memory. . Improve. . Replacement . Policies. Parallelize your code!. 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. 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.. 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. Niladrish Chatterjee. Mike O’Connor. Gabriel H. . Loh. Nuwan. . Jayasena. Rajeev . Balasubramonian. Irregular GPGPU Applications. Conventional GPGPU workloads access vector or matrix-based data structures. 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..

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