PPT-Approximating Warps with Intra-warp Operand

Value Similarity Daniel Wong Nam Sung Kim Murali Annavaram University of California Riverside dwongeceucredu University of Illinois Urbana Champagin. ©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. 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. 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: . : Sharing Requests with Inter-Warp Coalescing for Throughput Processors. John Kloosterman. , Jonathan Beaumont, Mick Wollman, Ankit . Sethia. , Ron . Dreslinski. , Trevor . Mudge. , Scott . Mahlke. Computer Engineering Laboratory. : Sharing Requests with Inter-Warp Coalescing for Throughput Processors. John Kloosterman. , Jonathan Beaumont, Mick Wollman, Ankit . Sethia. , Ron . Dreslinski. , Trevor . Mudge. , Scott . Mahlke. Computer Engineering Laboratory. Lecture 5: GPU Compute . Architecture. 1. Last time.... GPU Memory System. Different kinds of memory pools, caches, . etc. Different optimization techniques. 2. Warp Schedulers. Warp schedulers find a warp that is ready to execute its next instruction and available execution cores and then start execution. 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. ©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. 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. ©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. Lecture 5: GPU Compute . Architecture. 1. Last time.... GPU Memory System. Different kinds of memory pools, caches, . etc. Different optimization techniques. 2. Warp Schedulers. Warp schedulers find a warp that is ready to execute its next instruction and available execution cores and then start execution. 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. Reading. C. Kersey, “HARP Instruction Set Manual”. 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..