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Automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories

Muthu Manikandan Baskaran, Uday Bondhugula, Sriram Krishnamoorthy, J. Ramanujam, Atanas Rountev, P. Sadayappan

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Performance

February 28, 2011 by hgpu

A Domain-Specific Approach To Heterogeneous Parallelism

Hassan Chafi, Arvind K. Sujeeth, Kevin J. Brown, HyoukJoong Lee, Anand R. Atreya, Kunle Olukotun

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia GeForce GTX 275

February 27, 2011 by hgpu

Achieving a single compute device image in OpenCL for multiple GPUs

Jungwon Kim, Honggyu Kim, Joo H. Lee, Jaejin Lee

Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Optimization

February 25, 2011 by hgpu

An Automated Approach for SIMD Kernel Generation for GPU based Software Acceleration

Kanupriya Gulati and Sunil P Khatri

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280

February 21, 2011 by hgpu

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Pei-Hung Lin, Jagan Jayaraj, and Paul R. Woodward

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Tags: Cell processor, Code generation, Compilers, Computer science, CUDA, Fortran, High-level Languages, Memory model, nVidia, Tesla C1060

February 17, 2011 by hgpu

Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

K.A. Hawick and D.P. Playne

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Tags: Code generation, Compilers, Computer science, CUDA, Finite difference, nVidia, Partial differential equations, PDEs

February 14, 2011 by hgpu

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

K.A. Hawick and D.P. Playne

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Tags: Code generation, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 295, Partial differential equations, PDEs

February 13, 2011 by hgpu

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

D. P. Playne, K. A. Hawick

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Tags: Code generation, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX 480

February 13, 2011 by hgpu

Copperhead: Compiling an embedded data parallel language

Bryan Catanzaro, Michael Garland and Kurt Keutzer

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Tags: Code generation, Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 480, Package

February 13, 2011 by hgpu

Loop Transformation Recipes for Code Generation and Auto-Tuning

Mary Hall, Jacqueline Chame, Chun Chen, Jaewook Shin, Gabe Rudy, Malik Khan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenMP

February 12, 2011 by hgpu

Cg: a system for programming graphics hardware in a C-like language

William R. Mark, R. Steven Glanville, Kurt Akeley, Mark J. Kilgard

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Tags: 3D Graphics and Realism, Cg, Code generation, Compilers, Computer science, High-level Languages, nVidia, Package, Review

February 10, 2011 by hgpu

Automatic C-to-CUDA Code Generation for Affine Programs

Muthu Baskaran, J. Ramanujam, P. Sadayappan

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX

February 8, 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

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high performance computing on graphics processing units: hgpu.org

Automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories

A Domain-Specific Approach To Heterogeneous Parallelism

Achieving a single compute device image in OpenCL for multiple GPUs

An Automated Approach for SIMD Kernel Generation for GPU based Software Acceleration

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

Copperhead: Compiling an embedded data parallel language

Loop Transformation Recipes for Code Generation and Auto-Tuning

Cg: a system for programming graphics hardware in a C-like language

Automatic C-to-CUDA Code Generation for Affine Programs

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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