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A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Xinyu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado

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Tags: Algorithms, Computer science, Computer vision, Deep learning, DSP, FPGA, Neural networks

May 11, 2017 by hgpu

Resource-Aware Just-in-Time OpenCL Compiler for Coarse-Grained FPGA Overlays

Abhishek Kumar Jain, Douglas L. Maskell, Suhaib A. Fahmy

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Tags: ARM, Computer science, DSP, FPGA, Heterogeneous systems, OpenCL

May 11, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

Efficient Parallel Strategy Improvement for Parity Games

John Fearnley

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Logic in Computer Science, nVidia, nVidia GeForce GTX 780, Package

May 9, 2017 by hgpu

Fast Sorting Algorithms using AVX-512 on Intel Knights Landing

Berenger Bramas

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Tags: Algorithms, Computer science, Intel Xeon Phi, OpenMP, Package, Sorting

May 9, 2017 by hgpu

Acceleration of Deep Learning on FPGA

Huyuan Li

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Tags: CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, OpenCL, Tesla K40, Thesis

May 9, 2017 by hgpu

Multidimensional upwind hydrodynamics on unstructured meshes using Graphics Processing Units I. Two-dimensional uniform meshes

Sijme-Jan Paardekooper

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Tags: Algorithms, Astrophysics, cfd, Computational Physics, CUDA, Fluid dynamics, Instrumentation and Methods for Astrophysics, nVidia, Package, Tesla K20, Triangular meshes

May 9, 2017 by hgpu

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Antti-Pekka Hynninen, Dmitry I. Lyakh

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Tesla K20, Tesla K40, Tesla M40, Tesla P100

May 6, 2017 by hgpu

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee, Jeff Pool, Stephen W. Keckler

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Tags: Compression, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Virtualization

May 6, 2017 by hgpu

Numerical Model of Shallow Water: the Use of NVIDIA CUDA Graphics Processors

Tatyana Dyakonova, Alexander Khoperskov, Sergey Khrapov

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, Tesla C2070, Tesla K20, Tesla K40, Tesla K80

May 6, 2017 by hgpu

GPUQT: An efficient linear-scaling quantum transport code fully implemented on graphics processing units

Zheyong Fan, Ville Vierimaa, Ari Harju

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Tags: Computational Physics, CUDA, nVidia, Package, Physics, Sparse matrix, Tesla K40

May 6, 2017 by hgpu

AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters

Xiaojue Zhu, Everett Phillips, Vamsi Spandan, John Donners, Gregory Ruetsch, Josh Romero, Rodolfo Ostilla-Monico, Yantao Yang, Detlef Lohse, Roberto Verzicco, Massimiliano Fatica, Richard J.A.M. Stevens

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Tags: cfd, CUDA, Fluid dynamics, Fortran, GPU cluster, MPI, Navier-Stokes equations, NSEs, nVidia, Package, Tesla K20, Tesla P100, Tesla T40

May 6, 2017 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Parallel Gaussian process with kernel approximation in CUDA

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