Meta’s paper details how the company optimizes AI training on AMD GPUs by co‑designing hardware and models, using an analytical performance model for kernel selection, and extending agentic workflows for microarchitecture‑aware optimizations. The authors compare AMD Instinct MI350X to NVIDIA B200 on a recommendation model, achieving up to 1.5× higher training throughput. Their methods, the first hyperscaler report on productionizing AMD GPUs for large‑scale training, have been incorporated into open‑source PyTorch, Triton, and ROCm.
arXiv:2512. 23236v4 Announce Type: replace-cross Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important.
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:2608. 05033v1 Announce Type: cross Abstract: Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning.
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.
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.
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.
arXiv:2608. 01563v1 Announce Type: new Abstract: Training and deployed inference often cross export, conversion, and platform-specific runtime boundaries.
arXiv:2606. 04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code.
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: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.
arXiv:2609.36070v1 Announce Type: cross Abstract: AI accelerator systems are rapidly consolidating into scale-up architectures, where tens to thousands of GPUs communicate over high-bandwidth, single...
KernelArc is a multi-agent framework designed to autonomously optimize GPU kernels across diverse workloads. It employs strategy-specialized agents that run concurrently, coordinating via conclusions-only shared memory, a deterministic benchmark guard, and read-only cross-agent state with plateau-triggered drafting. Evaluated on NVIDIA H100 and B200 GPUs with SOL-ExecBench workloads, KernelArc produced top-ranked implementations for tasks such as BF16 GEMM, cuBLASLt configuration tables, and various attention mechanisms, achieving first place on several leaderboard categories.