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SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Gangwon Jo, Heehoon Kim, Jeesoo Lee, Jaejin Lee

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Tags: Compilers, Computer science, FPGA, OpenCL, Package, Performance

June 7, 2020 by hgpu

Deep Learning for Compilers

Chris Cummins

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Tags: AMD Radeon HD 7970, ATI, Benchmarking, Compilers, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Thesis

April 5, 2020 by hgpu

The Deep Learning Compiler: A Comprehensive Survey

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Compilers, Computer science, Deep learning, Machine learning, Performance

February 16, 2020 by hgpu

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

Andi Drebes, Lorenzo Chelini, Oleksandr Zinenko, Albert Cohen, Henk Corporaal, Tobias Grosser, Kanishkan Vadivel, Nicolas Vasilache

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Tags: Compilers, Computer science, CUDA, Machine learning

February 9, 2020 by hgpu

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

Ji Liu, Abdullah-Al Kafi, Xipeng Shen, Huiyang Zhou

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Tags: Compilers, Computer science, FPGA, nVidia, nVidia GeForce RTX 2080, OpenCL, Performance

February 9, 2020 by hgpu

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

Ari Rasch, Richard Schulze, Sergei Gorlatch

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Tags: Code generation, Compilers, Computer science, nVidia, OpenCL, Package, performance portability, Tesla V100

January 19, 2020 by hgpu

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Ryan Senanayake, Fredrik Kjolstad, Changwan Hong, Shoaib Kamil, Saman Amarasinghe

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Tags: Code generation, Compilers, Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, Package, Sparse matrix, Tesla V100

January 5, 2020 by hgpu

Compiler-Driven Performance on Heterogeneous Computing Platforms

Artem Chikin

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, Hybrid computing, nVidia, Performance, Tesla P100, Tesla V100, Thesis

November 17, 2019 by hgpu

Automatic Compiler Based FPGA Accelerator for CNN Training

Shreyas Kolala Venkataramanaiah, Yufei Ma, Shihui Yin, Eriko Nurvithadhi, Aravind Dasu, Yu Cao, Jae-sun Seo

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Tags: Code generation, Compilers, Computer science, Deep learning, FPGA, Neural networks

August 25, 2019 by hgpu

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

Paul Sathre, Mark Gardner, Wu-chun Feng

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Tags: AMD FirePro S9150, ATI, Compilers, Computer science, CUDA, FPGA, nVidia, OpenCL, Package, performance portability, Tesla K80

July 14, 2019 by hgpu

A Translation Framework from RVC-CAL Dataflow Programs to OpenCL/SYCL based Implementations

Florian Krebs

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Tags: Compilers, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 950 M, OpenCL, SYCL, Thesis

July 14, 2019 by hgpu

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Leyuan Wang, Zhi Chen, Yizhi Liu, Yao Wang, Lianmin Zheng, Mu Li, Yida Wang

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Tags: ARM, CNN, Compilers, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson Nano, OpenCL, Package

July 7, 2019 by hgpu

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BLOCKBENCH: A Framework for Analyzing Private Blockchains

Blockchain Goes Green? Part II: Characterizing the Performance and Cost of Blockchains on the Cloud and at the Edge

hls4ml: Machine learning on FPGAs using HLS

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

GPU-based JSON data processing using structural indexes

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Analytical Performance Estimation during Code Generation on Modern GPUs

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

The Celerity High-level API: C++20 for Accelerator Clusters

GPU-Sched

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Deep Learning for Compilers

The Deep Learning Compiler: A Comprehensive Survey

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

md_poly: A Performance-Portable Polyhedral Compiler Based on Multi-Dimensional Homomorphisms

A Unified Iteration Space Transformation Framework for Sparse and Dense Tensor Algebra

Compiler-Driven Performance on Heterogeneous Computing Platforms

Automatic Compiler Based FPGA Accelerator for CNN Training

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

A Translation Framework from RVC-CAL Dataflow Programs to OpenCL/SYCL based Implementations

A Unified Optimization Approach for CNN Model Inference on Integrated GPUs

Recent source codes

BLOCKBENCH: A Framework for Analyzing Private Blockchains

hls4ml: Machine learning on FPGAs using HLS

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

GPU-Sched

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