'Constant memory' presentation slideshows

CUDA

CUDA. All you wanted to know about it, but was afraid to ask!. Paulo Ivson Netto Santos Waldemar Celes Filho Nov 2007. CUDA is aimed at GPGPU. What is GPGPU ?. General Purpose computation using GPU Applications other than 3D graphics GPU accelerates critical path of application

2.22k views • 179 slides

Deterministic Finite Automata Machine

Deterministic Finite Automata Machine. More Examples of DFAs Simple examples 0*1* vs 0 n 1 n Mod Counting Examples Contains Substring (NFA vs DFA) Any Finite Language Integer mod 7 Adding to Integers Calculator Syntax Processor. Read-Once and Bounded Memory Loop Invariants

1.62k views • 141 slides

Accelerating Fast Fourier Transform for Wideband Channelization

1.37k views • 122 slides

Introduction to CUDA heterogeneous programming

Introduction to CUDA 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 1.15 x 1 x 448 = 515 Gigaflops double precision (peak)

799 views • 45 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

500 views • 35 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

Streamed Validation

Streamed Validation. Ksenia Rybenko, TU Dresden. XML-processing. Querying Computing running aggregates of streams Validating XML-documents against given DTDs. Statement of the problem.

539 views • 34 slides

CUDA ( C ompute U nified D evice A rchitecture)

By: Matt Sirabella Neil Weber Christian Casseus Jordan Dubique. CUDA ( C ompute U nified D evice A rchitecture). PARALLEL PROGRAMMING USING THE GPU. CUDA ( C ompute U nified D evice A rchitecture). History of Parallel Computing Computing using the GPU What is CUDA?

470 views • 32 slides

CS179: GPU Programming

CS179: GPU Programming. Lecture 8: More CUDA Runtime. Today. CUDA arrays for textures CUDA runtime Helpful CUDA functions. CUDA Arrays. Recall texture memory Used to store large data Stored on GPU Accessible to all blocks, threads. CUDA Arrays. Used Texture memory for buffers (lab 3)

482 views • 31 slides

CS179: GPU Programming

CS179: GPU Programming. Lecture 7: Lab 3 Recitation. Today. Miscellaneous CUDA syntax Recap on CUDA and buffers Shared memory for an N-body simulation Flocking simulations Integrators. CUDA Kernels. Launching the kernel: kernel<<< gridDim , blockDim , sMemSize >>>( args );

386 views • 23 slides

CS179: GPU Programming

CS179: GPU Programming. Lecture 7: Lab 3 Recitation. Today. Miscellaneous CUDA syntax Recap on CUDA and buffers Shared memory for an N-body simulation Flocking simulations Integrators. CUDA Kernels. Launching the kernel: kernel<<< gridDim , blockDim , sMemSize >>>( args );

355 views • 23 slides

Objective

338 views • 23 slides

ME964 High Performance Computing for Engineering Applications

316 views • 19 slides

Warp Speed

Warp Speed. A lighthearted introduction to GPGPUs Dr. Keith Schubert. Why Bother?. Jacquard Looms Again. Thread. Block. Grid. Registers (F) type var[n]; Local Memory(S) type var[n]; Index threadIdx.x. Shared Memory (F) __shared__ type var; Index blockIdx.x Size blockDim.x.

258 views • 17 slides

Back-Projection on GPU: Improving the Performance

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Fast Forwarding Table Lookup Exploiting GPU Memory Architecture

Fast Forwarding Table Lookup Exploiting GPU Memory Architecture. Date:100/12/14 Publisher:IEEE ICTC 2010 Author:Youngjun Lee,Minseon Jeong,Sanghwan Lee,Eun -Jin Im Kookmin University. Introduction.

282 views • 14 slides

GPU

GPU. Precision, Power, Programmability CPU: x60/decade, 6 GFLOPS, 6GB/sec GPU: x1000/decade, 20 GFLOPs, 25GB/sec Arithmetic heavy (read OR write): faster hardware Parallelization Multi-billion $ entertainment market drives innovation 32-bit Floating point

254 views • 12 slides

ITCS 4/5010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Jan 28, 2013 ConstantMemTiming

Using Constant Memory. These notes will introduce: How to declare and use constant memory Results of an experiment using constant memory. ITCS 4/5010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Jan 28, 2013 ConstantMemTiming.ppt. Global memory, shared memory, and registers. Host. Grid.

196 views • 11 slides

GPU Computing

244 views • 11 slides

Constant Memory

151 views • 4 slides