Is GPGPU CCL worth it? A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images | Alex Sundstrom, Victor Ahdel | Algorithms, AMD Radeon R9 280X, ATI, Computer science, Image processing, nVidia, nVidia GeForce GTX 960, OpenCL, Performance, Thesis
Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs | Joao Paulo Tarasconi Ruschel | Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis
Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). ZTE’s engineers used Intel’s midrange Arria 10 FPGA for a cloud inferencing application using a CNN algorithm. "ZTE has achieved a new record – beyond a thousand images per second in facial recognition – with what is known as “theoretical high accuracy” achieved for their custom topology. Intel’s Arria 10 FPGA accelerated the raw design performance more than 10 times while maintaining the accuracy."
Accelerating Workloads on FPGAs via OpenCL: A Case Study with OpenDwarfs | Anshuman Verma, Ahmed E. Helal, Konstantinos Krommydas, Wu-Chun Feng | Computer science, FPGA, Intel Xeon Phi, N-body simulation, nVidia, OpenCL, performance portability, Tesla C2070, Tesla K20
In this special guest feature, James Reinders looks at Intel Xeon Phi processors from a programmer's perspective. "How does a programmer think of Intel Xeon Phi processors? In this brief article, I will convey how I, as a programmer, think of them. In subsequent articles, I will dive a bit more into details of various programming modes, and techniques employed for some key applications. In this article, I will endeavor to not stray into deep details – but rather offer an approachable perspective on how to think about programming for Intel Xeon Phi processors."
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