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Gabriele Cocco, Antonio Cisternino
In the last few years there have been many activities towards coupling CPUs and GPUs in order to get the most from CPU-GPU heterogeneous systems. One of the main problems that prevent these systems to be exploited in a device-aware manner is the CPU-GPU communication bottleneck, which often doesn’t allow to produce code more efficient […]
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Naohito Nakasato
The kd-tree is a fundamental tool in computer science. Among other applications, the application of kd-tree search (by the tree method) to the fast evaluation of particle interactions and neighbor search is highly important, since the computational complexity of these problems is reduced from O(N^2) for a brute force method to O(N log N) for […]
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Rahul Garg, Jose Nelson Amaral
A new compilation framework enables the execution of numerical-intensive applications in an execution environment that is formed by multi-core Central Processing Units (CPUs) and Graphics Processing Units (GPUs). A critical innovation is the use of a variation of Linear Memory Access Descriptors (LMADs) to analyze loop nests and determine automatically which memory locations must be […]
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Naohito Nakasato
We present benchmark results of optimized dense matrix multiplication kernels for Cypress GPU. We write general matrix multiply (GEMM) kernels for single (SP), double (DP) and double-double (DDP) precision. Our SGEMM and DGEMM kernels show ~ 2 Top/s and ~ 470 Glop/s, respectively. These results for SP and DP correspond to 73% and 87% of […]
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Rodrigo Dominguez, Dana Schaa, David Kaeli
Graphics Processing Units (GPU) have become the platform of choice for accelerating a large range of data parallel and task parallel applications. Both AMD and NVIDIA have developed GPU implementations targeted at the high performance computing market. The rapid adoption of GPU computing has been greatly aided by the introduction of high-level programming environments such […]
Yi Shan, Tianji Wu, Yu Wang, Bo Wang, Zilong Wang, Ningyi Xu, Huazhong Yang
Sparse matrix-vector multiplication (SpMV) is a fundamental operation for many applications. Many studies have been done to implement the SpMV on different platforms, while few work focused on the very large scale datasets with millions of dimensions. This paper addresses the challenges of implementing large scale SpMV with FPGA and GPU in the application of […]
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Naohito Nakasato, Jun Makino
We introduce a newly developed compiler for high performance computing using many-core accelerators. A high peak performance of such accelerators attracts researchers who are always demanding faster computers. However, it is difficult to create an efficient implementation of an existing serial program for such accelerators even in the case of massively parallel problems. While existing […]
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Ryan Taylor, Xiaoming Li
Optimizing programs for Graphic Processing Unit (GPU) requires thorough knowledge about the values of architectural features for the new computing platform. However, this knowledge is frequently unavailable, e.g., due to insufficient documentation, which is probably a result of the infancy of general purpose computing on the GPU. What makes the modeling of program performance on […]
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Di Wu, Tianji Wu, Yi Shan, Yu Wang, Yong He, Ningyi Xu, Huazhong Yang
The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous […]
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Tianji Wu, Bo Wang, Yi Shan, Feng Yan, Yu Wang, Ningyi Xu
Google’s famous PageRank algorithm is widely used to determine the importance of web pages in search engines. Given the large number of web pages on the World Wide Web, efficient computation of PageRank becomes a challenging problem. We accelerated the power method for computing PageRank on AMD GPUs. The core component of the power method […]
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Naohito Nakasato
We present benchmark results of optimized dense matrix multiplication kernels for Cypress GPU. We write general matrix multiply (GEMM) kernels for single (SP), double (DP) and double-double (DDP) precision. Our SGEMM and DGEMM kernels show ~2 Tflop/s and ~470 Gflop/s, respectively. These results for SP and DP correspond to 73% and 87% of the theoretical […]
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Rob V. van Nieuwpoort and John W. Romein
Radio telescopes typically consist of multiple receivers whose signals are cross-correlated to filter out noise. A recent trend is to correlate in software instead of custom-built hardware, taking advantage of the flexibility that software solutions offer. Examples include e-VLBI and LOFAR. However, the data rates are usually high and the processing requirements challenging. Many-core processors […]
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