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AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek

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Tags: Algorithms, Code generation, Compilers, Computer science, Deep learning, HLS, LLVM, Programming Languages

January 20, 2019 by hgpu

Automatic acceleration of Numpy applications on GPUs and multicore CPUs

Mahesh Ravishankar, Vinod Grover

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Tags: Computer science, nVidia, Programming Languages

January 20, 2019 by hgpu

A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

Teng Wang, Chao Wang, Xuehai Zhou, Huaping Chen

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Tags: Computer science, Deep learning, FPGA, Neural networks, Review

January 20, 2019 by hgpu

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Aajna Karki, Chethan Palangotu Keshava, Spoorthi Mysore Shivakumar, Joshua Skow, Goutam Madhukeshwar Hegde, Hyeran Jeon

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, OpenCL, Package

January 20, 2019 by hgpu

Vulkan 1.1.97 – A Specification (with all registered Vulkan extensions)

The Khronos Vulkan Working Group

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Tags: Computer science, Vulkan, Whitepaper

January 13, 2019 by hgpu

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

B. P'erez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguad'e

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Tags: Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, Tesla K20

January 13, 2019 by hgpu

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

Jun Hyung Shin, Florin Rusu, Alex Suhan

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Tags: Computer science, CUDA, Databases, nVidia, Package, Tesla K80

January 13, 2019 by hgpu

BitCracker: BitLocker meets GPUs

Elena Agostini, Massimo Bernaschi

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Tags: AES, ATI, ATI Radeon HD 7990, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Security, Tesla K80, Tesla P100, Tesla V100

January 13, 2019 by hgpu

HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting

Zhuoran Ji

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Tags: Caffe, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, Package

January 13, 2019 by hgpu

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, Zhiru Zhang

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Tags: Algorithms, Compilers, Computer science, FPGA, Heterogeneous systems, HLS, OpenCL, Python

January 6, 2019 by hgpu

Vector and Line Quantization for Billion-scale Similarity Search on GPUs

Wei Chen, Jincai Chen, Fuhao Zou, Yuan-Fang Li, Ping Lu, Qiang Wang, Wei Zhao

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Tags: Computer science, CUDA, Databases, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X

January 6, 2019 by hgpu

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Periklis Chrysogelos, Manos Karpathiotakis, Raja Appuswamy, Anastasia Ailamaki

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Tags: Compilers, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080

January 6, 2019 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

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Automatic acceleration of Numpy applications on GPUs and multicore CPUs

A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

Tango: A Deep Neural Network Benchmark Suite for Various Accelerators

Vulkan 1.1.97 – A Specification (with all registered Vulkan extensions)

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

BitCracker: BitLocker meets GPUs

HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

Vector and Line Quantization for Billion-scale Similarity Search on GPUs

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

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)