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Citations References From the software perspective, a variety of optimization techniques BANK3 GeForce 8000 algorithm level [1,9,10,16,20,22] to compilation level [5,11,26, 31, 33] have been developed. GEMM is an important operator for many applications in a broad range of domains.
Many research works focus on its BANK3 GeForce 8000 from algorithm level [10,16,26] and architec- ture level [11, 31, 33]. These optimization techniques have been integrated into well optimized libraries . Matrix C is first divided into multiple tiles, and each thread block produces the partial result of each BANK3 GeForce 8000 [10,16,26]. Many works focus on low level GPU micro-architecture optimization [11, 31, 33].
Conference Paper. BANK3 GeForce 8000 Xiuhong Li. General matrix multiplication GEMM plays a paramount role in a broad range of domains such as deep learning, scientific computing, and image processing.
The primary optimization method is to partition the matrix into many tiles and exploit the parallelism within and between tiles. The tiling hierarchy closely mirrors BANK3 GeForce 8000 thread hierarchy on GPUs.
In practice, GPUs can fully unleash its computing power only when the matrix size is large and there are sufficient number of tiles and workload for each tile. However, in many real-world applications especially deep learning domain, the BANK3 GeForce 8000 size is small. However, the current support for batched GEMM is still rudimentary. Tiling and batching are tightly correlated. A large tile size can increase the data reuse, but it will decrease the thread-level parallelism, which further decrease the optimization space for the batching. A small tile BANK3 GeForce 8000 can increase the thread-level parallelism and then provide larger optimization space for the batching, but at the cost of sacrificing data reuse.
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Tiling engine partitions the GEMMs into independent tiles and batching engine assigns the tiles to thread blocks. Moreover, BANK3 GeForce 8000 propose a general programming interface for the coordinated tiling and batching solution. Finally, experiment evaluation results BANK3 GeForce 8000 synthetic batched GEMM cases show that our framework can achieve about 1. We also use GoogleNet as a real-world case study and our framework can achieve 1. A number of papers addresses specific GPU architectures [25,26]. Multiple papers perform a general analysis of a range of GPU architectures, reveal undisclosed details trough micro-benchmarking, and propose guidelines for performance optimization  .
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This information is invaluable to understand factors limiting performance on a specific architecture and to find BANK3 GeForce 8000 alternative approach to achieve a better performance. With the Kepler architecture, this scheme may even double the shared memory bandwidth by utilizing bit memory banks more efficiently. NVIDIA product manager Justin Walker discusses the GeForce Ultra GPU and the definitive gaming platform.
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