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CRINK: Automatic CUDA code generation for affine C programs

Akanksha Singh

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Tags: Code generation, Computer science, CUDA, nVidia, Tesla C1060, Thesis

August 10, 2015 by hgpu

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

Srajan Paliwal, Saurabh Tiwary, Bhaskar Chaudhury, Manish K. Gupta

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Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K40

July 22, 2015 by hgpu

Autotuning Tensor Contraction Computations on GPUs

Axel Rivera, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Thomas Nelson, Boyana Norris

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Tags: Benchmarking, Code generation, Computer science, CUDA, Matrix multiplication, nVidia, OpenACC, Tesla C2050, Tesla K20

June 19, 2015 by hgpu

Type-safe Runtime Code Generation: Accelerate to LLVM

Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, Ryan R. Newton

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Sparse matrix, Tesla K40

June 14, 2015 by hgpu

OMP2HMPP: Compiler Framework for Energy-Performance Trade-off Analysis of Automatically Generated Codes

Albert Saa-Garriga, David Castells-Rufas, Jordi Carrabina

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Tesla M2050

May 5, 2015 by hgpu

IPMACC: Translating OpenACC API to OpenCL

Ahmad Lashgar, Alireza Majidi, Amirali Baniasadi

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Tags: Code generation, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20

May 3, 2015 by hgpu

Loo.py: From Fortran to performance via transformation and substitution rules

Andreas Klockner

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Tags: Code generation, Computer science, Fortran, OpenCL, Package, Python

March 28, 2015 by hgpu

Generating Performance Portable Code using Rewrite Rules: From High-level Functional Expressions to High-Performance OpenCL Code

Michel Steuwer, Christian Fensch, Sam Lindley, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL

March 8, 2015 by hgpu

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Michel Steuwer, Christian Fensch, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Programming Languages

February 10, 2015 by hgpu

Pointer Analysis for Semi-Automatic Code Parallelizers

Ketan Sudhakar Vyavahare

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Tags: Code generation, Computer science, CUDA, OpenCL, Thesis

February 3, 2015 by hgpu

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Leonard Truong, Chick Markley, Armando Fox

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Tags: Code generation, Computer science, OpenCL, Python

January 8, 2015 by hgpu

Scalability and Optimization Strategies for GPU Enhanced Neural Networks (GeNN)

Naresh Balaji, Esin Yavuz, Thomas Nowotny

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Tags: Algorithms, Code generation, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GT 755 M, Package

December 2, 2014 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

CRINK: Automatic CUDA code generation for affine C programs

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

Autotuning Tensor Contraction Computations on GPUs

Type-safe Runtime Code Generation: Accelerate to LLVM

OMP2HMPP: Compiler Framework for Energy-Performance Trade-off Analysis of Automatically Generated Codes

IPMACC: Translating OpenACC API to OpenCL

Loo.py: From Fortran to performance via transformation and substitution rules

Generating Performance Portable Code using Rewrite Rules: From High-level Functional Expressions to High-Performance OpenCL Code

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Pointer Analysis for Semi-Automatic Code Parallelizers

An Extensible Framework for Composing Stencils with Common Scientific Computing Patterns

Scalability and Optimization Strategies for GPU Enhanced Neural Networks (GeNN)

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)