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Graphics processing units - powerful, programmable, and highly parallel - are increasingly targeting general-purpose computing applications. GPU Computing,[object Object],Presented By:,[object Object],Khan Muhammad Nafee Mostafa,[object Object],0507007, Dept of CSE, KUET,[object Object]
GPU Computing,[object Object],J. D. Owens,[object Object],M. Houston,[object Object],D. Luebke,[object Object],S. Green,[object Object],J. E. Stone,[object Object],J. C. Phillips,[object Object],Proceedings of the IEEE | Vol 96, No. 5 | May 2008,[object Object],We would be concentrating on,,[object Object],What is GPU Computing,[object Object],Why GPU Computing,[object Object],GPU Architecture and Evolution,[object Object],GPU Computing Model,[object Object],Software Environment ,[object Object],Future,[object Object]
GPU for General Purpose Computing,[object Object],What is GPU Computing ?,[object Object]
What is GPU Computing ?,[object Object],GPU computing is the use of a GPU to do general purpose scientific and engineering computing,[object Object],CPU and GPU together in a heterogeneous computing model.,[object Object],Sequential part of the application runs on the CPU and the computationally-intensive part runs on the GPU. ,[object Object],From the user’s perspective, the application just runs faster because it is using the high-performance of the GPU to boost performance. ,[object Object]
Over the past few years, the GPU has evolved from a fixed-function special-purpose processor into a full-fledged parallel programmable processor with additional fixed-function special-purpose functionality,[object Object],Why GPU Computing…,[object Object]
GPU for Non-Graphic Apps,[object Object],The GPU is designed for a particular class of applications with the following characteristics,,[object Object],Computational requirements are large,[object Object],Parallelism is substantial,[object Object],Throughput is more important than latency,[object Object],a growing community has identified other applications with similar characteristics and successfully mapped these applications onto the GPU,[object Object]
GPU extends its hand towards CPU for performance,[object Object],Parallelism is the future of computing,[object Object],Many applications have to process huge set of data following same functions,[object Object],Several stream processors can execute same  set of instructions on different data sets and give a higher throughput  ,[object Object],If GPU take some share of computation load from CPU, many applications can be benefitted in speed-up,[object Object]
GPU is now turned into a programmable engine,[object Object],GPU Architecture and Evolution,[object Object]
GPU Pipeline,[object Object],Available operations are configurable but not programmable,[object Object]
Evolution…,[object Object]
All GPU programs must be structured in this way: many parallel elements, each processed in parallel by a single program,[object Object],GPU Computing Model,[object Object]
Computing on the GPU,[object Object],Programming a GPU for Graphics,[object Object],programmer specifies geometry covering a screen region; rasterizer generates a fragment at each pixel location,[object Object],Each fragment is shaded by the fragment program (FP).,[object Object],FP computes the fragment by a combination of math operations and global memory reads,[object Object],resulting image can be used as texture on future passes.,[object Object]
Computing on the GPU,[object Object],Programming a GPU for Graphics,[object Object],Programming a GPU for General-Purpose Programs (Old),[object Object],programmer specifies geometric primitive covering computation domain of interest; rasterizer generates fragment,[object Object],Each fragment is shaded by an SPMD general purpose FP,[object Object],FP computes the fragment by a combination of math operations and ‘gather’ accesses from global memory. ,[object Object],resulting buffer can be used as an input on future passes. ,[object Object],programmer specifies geometry covering a screen region; rasterizer generates a fragment at each pixel location,[object Object],Each fragment is shaded by the fragment program (FP).,[object Object],FP computes the fragment by a combination of math operations and global memory reads,[object Object],resulting image can be used as texture on future passes.,[object Object]
Computing on the GPU,[object Object],Programming a GPU for General-Purpose Programs (New),[object Object],programmer directly defines the computation domain of interest as a structured grid of threads,[object Object],SPMD general-purpose program computes each thread,[object Object],each thread is computed by a combination of math  operations and both ‘gather’ (read) accesses from and ‘scatter’ (write) accesses to global memory; (same buffer can be used for both allowing more flexible algorithms),[object Object],resulting buffer in global memory can then be used as an input in future computation,[object Object]
Software Environments,[object Object]
Software Environments,[object Object],BrookGPU,[object Object],Microsoft’s Accelerator,[object Object],Vendor Specific GPGPU systems,[object Object],AMD ATI’s CTM (Close to the Metal),[object Object],NVIDIA’s CUDA (Compute Unified Device Architecture),[object Object]
Scan performance on CPU, graphics-based GPU (using OpenGL), and direct-compute GPU (using CUDA). Results obtained on a GeForce 8800 GTX GPU and Intel Core2-Duo Extreme 2.93 GHz CPU. ,[object Object],(Figure adapted from Harris et al.),[object Object],Scan performance on CPU, OpenGL and CUDA,[object Object]
Future…,[object Object]
Concluding for bright Future…,[object Object],support for double-precision floating-point,[object Object],higher bandwidth path between CPU and GPU (like ATI’s HyperTransport),[object Object],more tightly coupled CPU and GPU (AMD’s fusion or nVidianForce),[object Object],NVIDIA Quadro for Multiple GPU Collaboration,[object Object],Finally, let us wait for new era when GPU Computing will rule,[object Object]
Thank You,[object Object],I would also like to thank,,[object Object]

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GPU Computing

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