high performance computing on graphics processing units: hgpu.org

Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

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Tags: Astrophysics, Cosmology, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Package, TensorFlow, Tesla P100

August 19, 2018 by hgpu

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package

August 19, 2018 by hgpu

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Daniel Hanlon, Hamidreza Khalighzadeh, Ravi Reddy Manumachu, Alexey Lastovetsky

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Tags: Cloud, Computer science, CUDA, FPGA, Hybrid computing, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package

August 19, 2018 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

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high performance computing on graphics processing units: hgpu.org

Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU

Doctor AI: Interpretable Deep Learning for Modeling Electronic Health Records

Cracks in the Sky: Abelian-Higgs Cosmic String Evolution with CUDA

Implementing Strassen’s Algorithm with CUTLASS on NVIDIA Volta GPUs

Full Speed Ahead: 3D Spatial Database Acceleration with GPUs

Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU

Performance Evaluation of OpenMP’s Target Construct on GPUs – Exploring Compiler Optimizations

A Qualitative Comparison Study Between Common GPGPU Frameworks

Kernel Tuner: A search-optimizing GPU code auto-tuner

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

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