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PoCL-R: A Scalable Low Latency Distributed OpenCL Runtime

Jan Solanti, Michal Babej, Julius Ikkala, Vinod Kumar Malamal Vadakital, Pekka Jääskeläinen

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Tags: Computer science, GPU cluster, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package, Rendering, Tesla P100, Tesla V100

August 8, 2021 by hgpu

Effective GPU Sharing Under Compiler Guidance

Chao Chen, Chris Porter, Santosh Pande

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Tags: Compilers, Computer science, CUDA, LLVM, Neural networks, nVidia, Tesla P100, Tesla V100

July 25, 2021 by hgpu

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

Enrico Tabanelli, Giuseppe Tagliavini, Luca Benini

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Tags: Computer science, FPGA, Machine learning, Neural networks, nVidia, Package, SoC, Tesla A100

July 25, 2021 by hgpu

Designing a high-performance boundary element library with OpenCL and Numba

T. Betcke, M. W. Scroggs

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Tags: Computer science, HPC, nVidia, nVidia Quadro RTX 3000, OpenCL, Package, PyOpenCL, Python

July 18, 2021 by hgpu

Optimisation and GPU code generation of Stencils for Futhark

Christian Charlie Virt, Jonathan Wraa-Hansen

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 780, nVidia GeForce RTX 2080 Ti, OpenCL, Performance, Thesis

July 18, 2021 by hgpu

GPTPU: Accelerating Applications using Edge Tensor Processing Units

Kuan-Chieh Hsu, Hung-Wei Tseng

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Machine learning, Neural networks, nVidia, OpenCL, TPU

July 18, 2021 by hgpu

Block Conjugate Gradient Solver in OpenCL

Kert Tali, Eero Vainikko

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Tags: Algorithms, Computer science, Conjugate gradient solver, Linear Algebra, nVidia, nVidia GeForce RTX 2080, OpenCL

July 11, 2021 by hgpu

KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks

J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian Foster, Zhao Zhang

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia A100, nVidia DGX-A100, Package, Tesla V100

July 11, 2021 by hgpu

Dynamic Adaptation Techniques and Opportunities to Improve HPC Runtimes

Mohammad Alaul Haque Monil

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

July 11, 2021 by hgpu

Opening the Black Box: Performance Estimation during Code Generation for GPUs

Dominik Ernst, Georg Hager, Markus Holzer, Matthias Knorr, Gerhard Wellein

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Tags: Code generation, Computer science, CUDA, Lattice Boltzmann model, Machine learning, nVidia, Performance, Stencil computation, Tesla V100

July 11, 2021 by hgpu

A Sorting Library for FPGA Implementation in OpenCL Programming

Ryohei Kobayashi, Kento Miura, Norihisa Fujita, Taisuke Boku, Toshiyuki Amagasa

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Tags: Algorithms, Computer science, FPGA, nVidia, OpenCL, Sorting, Tesla V100

July 4, 2021 by hgpu

Productivity, Portability, Performance: Data-Centric Python

Alexandros Nikolaos Ziogas, Timo Schneider, Tal Ben-Nun, Alexandru Calotoiu, Tiziano De Matteis, Johannes de Fine Licht, Luca Lavarini, Torsten Hoefler

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Tags: Computer science, FPGA, HPC, nVidia, Python, Tesla V100

July 4, 2021 by hgpu

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

True 4-Bit Quantized CNN Training on CPU

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

Hunting CUDA Bugs at Scale with cuFuzz

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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