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Machine learning enhanced code optimization for high-level synthesis (ML-ECOHS)

Robert P. Munafo

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Tags: Compilers, Computer science, FPGA, HLS, Machine learning, OpenCL, Package, Thesis

June 2, 2024 by hgpu

CuPBoP: Making CUDA a Portable Language

Ruobing Han, Jun Chen, Bhanu Garg, Xule Zhou, John Lu, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, HIP, nVidia, nVidia A30, OpenCL, Package, Portability, SYCL

May 12, 2024 by hgpu

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

Massinissa Merouani, Khaled Afif Boudaoud, Iheb Nassim Aouadj, Nassim Tchoulak, Islam Kara Bernou, Hamza Benyamina, Fatima Benbouzid-Si Tayeb, Karima Benatchba, Hugh Leather, Riyadh Baghdadi

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Tags: Compilers, Computer science, Deep learning, Machine learning, OpenCL, Package, Programming Languages

March 24, 2024 by hgpu

Parallel programming in mobile devices with FancyJCL

Sergio Afonso, Óscar Gómez-Cárdenes, Paula Expósito, Vicente Blanco, Francisco Almeida

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Tags: Computer science, DSP, Heterogeneous systems, Image processing, Java, OpenCL, Package

March 3, 2024 by hgpu

Analyzing GPU Performance in Virtualized Environments: A Case Study

Adel Belkhiri, Michel Dagenais

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Tags: Computer science, CUDA, Java, nVidia, OpenCL, Package, Performance, Virtualization

February 25, 2024 by hgpu

Deductive verification for SYCL

Ellen Wittingen

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Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, OpenCL, OpenMP, Package, SYCL, Thesis

February 4, 2024 by hgpu

LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory

Tyler Sorensen, Heidy Khlaaf

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Tags: Computer science, Data recovery, nVidia, nVidia GeForce RTX 4070, OpenCL, Package, Security, Vulkan

February 4, 2024 by hgpu

Lessons Learned Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame

Jolly Chen, Monica Dessole, Ana Lucia Varbanescu

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Tags: CUDA, HEP, nVidia, nVidia Quadro RTX 4000, OpenCL, Package, Performance, Physics, SYCL

January 28, 2024 by hgpu

Code Generation for a Variety of Accelerators for a Graph DSL

Ashwina Kumar, M. Venkata Krishna, Prasanna Bartakke, Rahul Kumar, Rajesh Pandian M, Nibedita Behera, Rupesh Nasre

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Tags: Code generation, Computer science, CUDA, DSL, nVidia, nVidia GeForce RTX 2080 Ti, OpenACC, OpenCL, Package, SYCL, Tesla V100

January 14, 2024 by hgpu

Deep Learning for Obfuscated Code Analysis

Alexander Shroyer

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 2060, OpenCL, Package, PyTorch, Thesis

January 7, 2024 by hgpu

Gaiwan: a Size-Polymorphic Typesystem for GPU Programs

Robbert Gurdeep Singha, Christophe Scholliers

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Tags: Benchmarking, Computer science, nVidia, nVidia A100, OpenCL, Package, Programming Languages

December 31, 2023 by hgpu

Application Performance Profiling on Intel GPUs with Oneprof and Onetrace

Mariam Umar, Maxwell Jong

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Tags: Computer science, Intel, Intel Data Center GPU Max 1550, OpenCL, OpenMP, Package, Performance

December 18, 2023 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

Programming

Machine learning enhanced code optimization for high-level synthesis (ML-ECOHS)

CuPBoP: Making CUDA a Portable Language

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

Parallel programming in mobile devices with FancyJCL

Analyzing GPU Performance in Virtualized Environments: A Case Study

Deductive verification for SYCL

LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory

Lessons Learned Migrating CUDA to SYCL: A HEP Case Study with ROOT RDataFrame

Code Generation for a Variety of Accelerators for a Graph DSL

Deep Learning for Obfuscated Code Analysis

Gaiwan: a Size-Polymorphic Typesystem for GPU Programs

Application Performance Profiling on Intel GPUs with Oneprof and Onetrace

Recent source codes

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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