high performance computing on graphics processing units: hgpu.org

hgpu.org » Mixed precision

Optimising Reconfigurable Systems for Real-time Applications

Thomas Chun Pong Chau

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, Mixed precision, nVidia, Package, Tesla C2070, Thesis

August 7, 2015 by hgpu

Towards an automatic generation of dense linear algebra solvers on parallel architectures

Marc Baboulin, Joel Falcou, Ian Masliah

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Tags: Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, Tesla C2075

December 20, 2014 by hgpu

Mixed-precision orthogonalization scheme and its case studies with CA-GMRES on a GPU

Ichitaro Yamazaki, Stanimire Tomov, Tingxing Dong, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Mixed precision, nVidia, Tesla K20, Tesla K40, Tesla M2090

July 29, 2014 by hgpu

Adjoint Algorithmic Differentiation of a GPU Accelerated Application

Jacques du Toit, Johannes Lotz, Uwe Naumann

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Tags: Algorithms, Computer science, CUDA, Mixed precision, Monte Carlo simulation, nVidia, Tesla K20

November 2, 2013 by hgpu

Efficient CUDA polynomial preconditioned Conjugate Gradient solver for Finite Element computation of elasticity problems

Jianfei Zhang, Lei Zhang

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Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, Mixed precision, nVidia, nVidia GeForce GT 430, Sparse matrix

September 14, 2013 by hgpu

Local Volatility FX Basket Option on CPU and GPU

Jacques du Toit, Isabel Ehrlich

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Tags: CUDA, Finance, Mixed precision, nVidia, Tesla K20

May 19, 2013 by hgpu

Coulomb, Landau and Maximally Abelian Gauge Fixing in Lattice QCD with Multi-GPUs

Mario Schrock, Hannes Vogt

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Tags: CUDA, GPU cluster, High Energy Physics - Lattice, Mixed precision, Monte Carlo simulation, MPI, nVidia, Physics, QCD, Tesla C2070

December 23, 2012 by hgpu

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Hartwig Anzt, Maribel Castillo, Juan C. Fernandez, Vincent Heuveline, Francisco D. Igual, Rafael Mayo and Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

January 11, 2012 by hgpu

GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement

Hartwig Anzt, Piotr Luszczek, Jack Dongarra, Vincent Heuveline

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Tags: Algorithms, Computer science, CUDA, Mixed precision, nVidia, Tesla C2070

January 6, 2012 by hgpu

Power consumption of mixed precision in the iterative solution of sparse linear systems

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

December 14, 2011 by hgpu

An error correction solver for linear systems: Evaluation of mixed precision implementations

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker

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Tags: Benchmarking, Computer science, CUDA, Mixed precision, nVidia, Tesla S1070

December 2, 2011 by hgpu

Architecture-Aware Algorithms and Software for Peta and Exascale Computing

Jack J. Dongarra

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Tags: Algorithms, Computer science, Mixed precision, MPI, Review

November 30, 2011 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_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

See all packages

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high performance computing on graphics processing units: hgpu.org

Optimising Reconfigurable Systems for Real-time Applications

Towards an automatic generation of dense linear algebra solvers on parallel architectures

Mixed-precision orthogonalization scheme and its case studies with CA-GMRES on a GPU

Adjoint Algorithmic Differentiation of a GPU Accelerated Application

Efficient CUDA polynomial preconditioned Conjugate Gradient solver for Finite Element computation of elasticity problems

Local Volatility FX Basket Option on CPU and GPU

Coulomb, Landau and Maximally Abelian Gauge Fixing in Lattice QCD with Multi-GPUs

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement

Power consumption of mixed precision in the iterative solution of sparse linear systems

An error correction solver for linear systems: Evaluation of mixed precision implementations

Architecture-Aware Algorithms and Software for Peta and Exascale Computing

Recent source codes

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

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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