Code generation | Page 24

hgpu.org » Code generation

An Energy Consumption Model for GPU Computing at Instruction Level

Haifeng Wang, Qingkui Chen

View

Download (PDF)

Tags: Code generation, Computer science, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, PTX

February 27, 2012 by hgpu

Introducing ‘Bones’: A Parallelizing Source-to-Source Compiler Based on Algorithmic Skeletons

Cedric Nugteren, Henk Corporaal

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 5850, Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, OpenCL

February 20, 2012 by hgpu

Skeleton-based Automatic Parallelization of Image Processing Algorithms for GPUs

Cedric Nugteren, Henk Corporaal, Bart Mesman

View

Download (PDF)

Tags: Algorithms, Code generation, CUDA, Image processing, nVidia, nVidia GeForce GTX 470, Optimization

February 17, 2012 by hgpu

Comparing Many-Core Accelerator Frameworks

G. Haase, A. Kucher

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Optimization

February 15, 2012 by hgpu

Symbolic Testing of OpenCL Code

Peter Collingbourne, Cristian Cadar, Paul H. J. Kelly

View

Download (PDF)

Source codes

Tags: Benchmarking, Code generation, Computer science, nVidia, OpenCL, Package, PTX, Tesla C1060

February 8, 2012 by hgpu

Decompilation of LLVM IR

Simon Moll

View

Download (PDF)

Tags: Code generation, Compilers, Computer science, GLSL, HLSL, Java, nVidia, nVidia GeForce 9600 GT, OpenCL, Optimization, Thesis

January 31, 2012 by hgpu

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Johan Enmyren, Usman Dastgeer, Christoph W. Kessler

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce GTS 250, OpenCL, Performance, Tesla C1060

January 30, 2012 by hgpu

Automatic Code Generation and Adaptive Grid Scheduling for GPU Cluster Computing

Xuyuan Jin

View

Download (PDF)

Tags: Cluster computing, Code generation, Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 570

January 21, 2012 by hgpu

Providing Source Code Level Portability Between CPU and GPU with MapCG

Chun-Tao Hong, De-Hao Chen, Yu-Bei Chen, Wen-Guang Chen, Wei-Min Zheng, Hai-Bo Lin

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL

January 9, 2012 by hgpu

CodePy

Martin Kempf

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, CUDA, nVidia, Package, Python

January 6, 2012 by hgpu

PARRAY: A Unifying Array Representation for Heterogeneous Parallelism

Yifeng Chen, Xiang Cui, Hong Mei

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, FFT, GPU cluster, Heterogeneous systems, MPI, nVidia, Optimization, Performance, Programming techniques, Pthreads, Tesla C1060

January 5, 2012 by hgpu

Automatic SIMD Code Generation

Roland Leissa

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, Optimization, Performance, Software Engineering, Thesis

January 4, 2012 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

* * *

high performance computing on graphics processing units: hgpu.org

An Energy Consumption Model for GPU Computing at Instruction Level

Introducing ‘Bones’: A Parallelizing Source-to-Source Compiler Based on Algorithmic Skeletons

Skeleton-based Automatic Parallelization of Image Processing Algorithms for GPUs

Comparing Many-Core Accelerator Frameworks

Symbolic Testing of OpenCL Code

Decompilation of LLVM IR

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Automatic Code Generation and Adaptive Grid Scheduling for GPU Cluster Computing

Providing Source Code Level Portability Between CPU and GPU with MapCG

CodePy

PARRAY: A Unifying Array Representation for Heterogeneous Parallelism

Automatic SIMD Code Generation

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

Most viewed papers (last 30 days)