PPT-Installing CUDA, PyCUDA

on Ubuntu Cuda download site httpsdevelopernvidiacomcudadownloads sudo dpkg i cudarepoubuntu14047518amd64deb sudo aptget update sudo aptget install . A1 Installing R 1 Go to httpwwwrprojectorg 2 Click on the download R link 3 Then select a location closest to you 4 Click on your operating system Linux MacOS X Windows and follow direc tions If you are a Mac user download the latest dmg 64257le and heterogeneous programming. Katia Oleinik. koleinik@bu.edu. Scientific Computing and Visualization. Boston . University. Architecture. NVIDIA Tesla M2070: . Core clock: 1.15GHz . Single instruction . 448 CUDA cores . 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). 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.. and Debugging. Shehzan. ArrayFire. Summary. ArrayFire. GPU Programming and CUDA. Debugging and Profiling using CUDA Tools. Memory Coalescing. Shared Memory and Bank Conflicts. Transpose. Reduction. ArrayFire. Overview. GPU Ocelot overview. Building, configuring, and executing Ocelot programs. Ocelot Device Interface and CUDA Runtime API. Ocelot PTX Internal Representation. PTX Pass Manager. 2. Ocelot: Multiplatform Dynamic Compilation. Performance considerations. (CUDA best practices) . NVIDIA CUDA C programming best practices guide. ACK: CUDA teaching center Stanford (. Hoberrock. and . Tarjan. ).. Outline. Host to device memory transfer. introduce the use of multiple CUDA streams to overlap memory transfers with kernel computations.. Also introduced is paged-locked memory. 2. Page-locked host memory. (also called pinned host memory). heterogeneous programming. Brian Gregor. bgregor@bu.edu. Research Computing Services. Boston University. CUDA C/C BASICS. NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. What is CUDA?. Data Parallelism. Host-Device model. Thread execution. Matrix-multiplication . GPU revised!. What is CUDA?. C. ompute . D. evice . U. nified . A. rchitecture. Programming interface to GPU. 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