arXiv AI
23h ago
KernelArc: A Multi-Agent Framework for GPU Kernel Optimization
arXiv:2608. 17071v1 Announce Type: new Abstract: We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads.
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
arXiv:2608. 07078v1 Announce Type: cross Abstract: Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale.
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arXiv Machine Learning
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MegaFold: Efficient Training of Next-Generation 3D Attention Protein Models on Cross-Platform GPUs
arXiv:2506. 20686v2 Announce Type: replace-cross Abstract: Recent advances in biomolecular modeling have been catalyzed by models such as AlphaFold3 (AF3), which introduce science-informed changes to the transformer architecture.
arXiv Machine Learning
Aug 6
SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System
arXiv:2608. 05033v1 Announce Type: cross Abstract: Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning.
arXiv AI
Jun 3
FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs
arXiv:2506. 01969v3 Announce Type: replace-cross Abstract: Efficient inference of Multi-Head Latent Attention (MLA) is challenged by deploying the DeepSeek-R1 671B model on a single Multi-GPU server.
arXiv Machine Learning
Jul 10
Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
arXiv:2604. 10180v2 Announce Type: replace-cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters.
arXiv Machine Learning
Jul 10
CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
arXiv:2607. 07862v1 Announce Type: cross Abstract: The evolution of compute infrastructure has transformed multi-GPU systems into tightly integrated shared-memory structures.
arXiv AI
Jul 21
KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?
arXiv:2607. 16241v1 Announce Type: cross Abstract: Recent large language models (LLMs) can generate custom CUDA kernels that appear to outperform PyTorch on benchmarks such as KernelBench.
arXiv AI
Jul 7
Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving
arXiv:2602. 24044v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of adapters must be hosted concurrently.
arXiv AI
Jul 17
Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent
arXiv:2607. 14541v1 Announce Type: new Abstract: Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads.
arXiv Machine Learning
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CAKE: Compiler-Agent Co-Design for Frontier Kernel Evolution
arXiv:2608. 12629v1 Announce Type: new Abstract: GPU kernel agents and GPU programming languages have advanced separately, leaving expert kernels difficult to reproduce.
arXiv Machine Learning
Aug 12
CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening
arXiv:2608. 10506v1 Announce Type: cross Abstract: Accurate pre-deployment estimation of CNN inference cost--energy, latency, and peak memory--is increasingly critical as models are deployed on resource-constrained GPU platforms.
arXiv AI
Jul 29
Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels
arXiv:2607. 24762v1 Announce Type: new Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization.