PPT-Taking CUDA to Ludicrous Speed

Getting Righteous Performance from your GPU Optimizing on CPUs Could I be getting better performance Probably a little bit but most of it is hidden from the user How much better If you compile O3 you can get faster maybe 2x. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). 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. CUDA Simulation. Benjy. Kessler. Given a brittle substance with a crack in it.. The goal is to study how the crack propagates in the substance as a function of time.. This accomplished by simulating the substance as a grid of points with forces acting upon them.. GPGPU Programming in CUDA. Supada . Laosooksathit. NVIDIA Hardware Architecture. Host. memory. Recall. 5 steps for CUDA Programming. Initialize device. Allocate. device memory. Copy. data to device memory. NVIDIA Corporation. Tesla GPU Computing. A Revolution in High Performance Computing. Agenda. CUDA Review. Architecture. Programming Model. Memory Model. CUDA C. CUDA General Optimizations. Fermi. Next Generation Architecture. Applications:. NAMD. Parallel Framework for Unstructured Meshing (. ParFUM. ). Features:. Profile snapshots:. Captures the runtime of the application by segregating it into user specified intervals. CUDA Profiling. Proposed Work. This . work aims . to enable efficient dynamic memory management on NVIDIA GPUs by utilizing a sub-allocator between CUDA and the programmer. This work enables Many-Task Computing applications, which need to dynamically allocate parameters for each task, to run efficiently on GPUs.. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. Introduction to Programming Massively Parallel Graphics processors. Andreas . Moshovos. moshovos@eecg.toronto.edu. ECE, Univ. of Toronto. Summer 2010. Some slides/material from:. UIUC course by . Wen. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). Men’s Olympic Speed Skating What is speed skating? Speed skating is a competitive form of ice skating where competitors race each other in travelling a certain distance on skates. Men compete in 500m, 1,000m, 1,500m, 5,000m and a 10,000. In each event, skaters race in pairs against the clock on a standard 400m oval ring. All events are skated once, apart from the 500m, which is skated twice. In this case, the final result is based on the total time of the two races. Defines much more than an API. A language . Hardware Specifications. PA0. Let’s look into your first assignment and figure some things out.. HELLOCUDA.CU. HELLOCUDA.CU. Pointers to GPU land. dev_a. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat