'Bank conflicts' diaporamas de présentation

Turning the Crank on Streaming Algorithms 20 Nov 2013, Markham, ON IBM CASCON 2013

Turning the Crank on Streaming Algorithms 20 Nov 2013, Markham, ON IBM CASCON 2013. Sean Baxter. Productivity with CUDA. Out the door and on to the next problem. Agenda. Streaming algorithm overview Two-phase decomposition. Scan Parallel communication . Merge Parallel partitioning .

742 views • 62 slides

Advanced Data-Parallel Programming: Data Structures and Algorithms

Advanced Data-Parallel Programming: Data Structures and Algorithms. John Owens UC Davis. One Slide Summary of Today. GPUs are great at running many closely-coupled but independent threads in parallel The programming model specifies a kernel program over independent threads

747 views • 55 slides

A Case for Subarray -Level Parallelism (SALP) in DRAM

A Case for Subarray -Level Parallelism (SALP) in DRAM. Yoongu Kim , Vivek Seshadri , Donghyuk Lee, Jamie Liu, Onur Mutlu. Executive Summary. Problem : Requests to same DRAM bank are serialized Our Goal : P arallelize requests to same DRAM bank at a low cost

483 views • 48 slides

Lecture 5: Performance Considerations

483 views • 48 slides

GPU Computing Techniques

GPU Computing Techniques. Using CUDA. CUDA & the GPU:. Host Interface & Giga Thread Engine. Raster Engine. Raster Engine. GPU Cores. GPU Cores. Memory Controller. Shared L1 Cache. GPU Cores. GPU Cores. Raster Engine. CUDA & the GPU:. Host Interface & Giga Thread Engine.

595 views • 42 slides

Automated Dynamic Analysis of CUDA Programs

Automated Dynamic Analysis of CUDA Programs. Michael Boyer, Kevin Skadron*, and Westley Weimer University of Virginia {boyer,skadron,weimer}@cs.virginia.edu * currently on sabbatical with NVIDIA Research. Outline. GPGPU CUDA Automated analyses Correctness: race conditions

492 views • 36 slides

CS179: GPU Programming

CS179: GPU Programming. Lecture 5: Memory. Today. GPU Memory Overview CUDA Memory Syntax Tips and tricks for memory handling. Memory Overview. Very slow access: Between host and device Slow access: Global Memory Fast access: Shared memory, constant memory, texture memory, local memory

498 views • 35 slides

ME964 High Performance Computing for Engineering Applications

468 views • 35 slides

CUDA Workshop, Week 4

476 views • 34 slides

Introduction To GPUs

339 views • 20 slides

Memory Intensive Benchmarks: IRAM vs. Cache Based Machines

Memory Intensive Benchmarks: IRAM vs. Cache Based Machines. Parry Husbands (LBNL) Brain Gaeke, Xiaoye Li, Leonid Oliker, Katherine Yelick (UCB/LBNL), Rupak Biswas (NASA Ames). Motivation.

235 views • 17 slides