From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

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.

CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe

arXiv:2604. 01489v2 Announce Type: replace Abstract: High-performance GPU kernels are critical to modern machine learning systems, yet developing them remains a manual, expert-driven process.

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.

Accelerate ND-Parallel: A guide to Efficient Multi-GPU Training

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.

GPU-Resident Top-K for Agentic RAG: I Built a CUDA Kernel So My Retrieval Step Would Stop Bouncing Off the GPU

The PCIe transfer latency is silently bottlenecking your agentic inference. Here is how building a custom device-resident vector search kernel bypasses the CPU to unlock deterministic microsecond tail latencies.

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

arXiv:2603. 16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

arXiv:2604. 23466v2 Announce Type: replace Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs.

DataKernelBench: Can LLMs Optimize Database Queries on GPUs?

DataKernelBench evaluates whether large language models (LLMs) can optimize database queries for GPU execution. The benchmark translates SQL into PyTorch TorchPlan programs and tests LLMs on optimizing core tensor snippets or full queries in CUDA or Triton, using execution-guided repair. On TPC‑H SF10 with an H100 GPU, the best full‑query CUDA configuration outperforms torch.compile by 2.11×, and extending TorchPlan with Dask‑cuDF enables a 2.54× speedup on TPC‑H SF100 across four H100 GPUs.

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.

We Got Claude to Build CUDA Kernels and teach open models!

KernelFoundry: Hardware-aware evolutionary GPU kernel optimization

KernelFoundry is a hardware‑aware evolutionary framework that optimizes GPU kernels by combining MAP‑Elites quality‑diversity search, meta‑prompt evolution, and template‑based parameter tuning. It generates SYCL and CUDA kernels, outperforming baseline methods with an average 2.3× speedup on KernelBench. The system is distributed, supports remote hardware access, and offers a flexible user interface for diverse real‑world kernel generation tasks.