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Springald: GPU-Accelerated Window-Based Aggregates Over Out-of-Order Data Streams

Gabriele Mencagli, Patrizio Dazzi, Massimo Coppola

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Tags: Computer science, CUDA, DSP, nVidia, nVidia A30, Package, Stream Computing

August 4, 2024 by hgpu

Brook for GPUs: Stream Computing on Graphics Hardware

I. Buck, T. Foley, D. Horn, J. Sugerman, K. Mike, H. Pat

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Tags: ATI, ATI Radeon 9800 XT, ATI Stream, Brook, Computer science, High-level Languages, nVidia, nVidia GeForce FX 5900 Ultra, OpenGL, Stream Computing

November 3, 2010 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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