PPT-Anatomy of GPU Memory System for

MultiApplication Execution Adwait Jog 1 Onur Kayiran 2 Tuba Kesten 3 Ashutosh Pattnaik 3 Evgeny Bolotin 4 Nil Chatterjee 4 Steve Keckler 45 Mahmut Kandemir 3 Chita Das. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. Dr A . Sahu. Dept of Comp Sc & . Engg. . . IIT . Guwahati. 1. Outline. Graphics System . GPU Architecture. Memory Model. Vertex Buffer, Texture buffer. GPU Programming Model. DirectX. , OpenGL, . Instructor Notes. This lecture begins with an example of how a wide-memory bus is utilized in . GPUs. The impact of memory coalescing and memory bank conflicts are also described along with examples on current hardware. Alan . Gray. EPCC . The University of Edinburgh. Outline. Why do we want/need accelerators such as GPUs?. Architectural reasons for accelerator performance advantages . Latest accelerator Products. NVIDIA and AMD GPUs. David Gilbert. California State University, Los Angeles. Outline. CUDA. CPU . vs. GPU Architecture. Scalability. Blocks. Performance. Speed Up. Graphics Cards. How It Works. Program Flow. When to Use the GPU. Lecture 2: more basics. Recap. Can use GPU to solve highly parallelizable problems. Straightforward extension to C++. Separate CUDA code into .cu and .. cuh. files and compile with . nvcc. to create object files (.o files). 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. Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Evangelia A. Sitaridi, Kenneth A. Ross. Columbia University. DaMoN Workshop. 21st May 2012. . Introduction (1/2). Earlier GPU implementations of data processing operators have resulted in significant speedups. Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU- accelerable algorithms CUDA libraries and tools This Lecture GPU- accelerable algorithms: Reduction Prefix sum Stream compaction Sorting (quicksort) Lecture 7. Last Week. Memory optimizations using different GPU caches. Atomic operations. Synchronization with __. syncthreads. (). Week 3. Advanced GPU-accelerable algorithms. “Reductions” to parallelize problems that don’t seem intuitively parallelizable. Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz. structural . organisation. Anatome. (. ana. =up. . tomy. =cut) . Anatomise. Dissection. Practical applied science which forms firm foundation of art of healing(medicine).