arXiv AI
Aug 10
A Fortran General-Purpose Transpiler: Proof of Concept
arXiv:2608. 00130v2 Announce Type: replace-cross Abstract: Fortran has been the cornerstone of high-performance computing for decades and remains unmatched in many domains.
From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow
The paper introduces an autonomous AI workflow that uses specialized large language model agents to translate legacy Fortran code into performance‑portable Kokkos C++ programs. The pipeline handles translation, validation, compilation, execution, testing, debugging, and optimization, and has been shown to modernize benchmark kernels across heterogeneous GPU‑accelerated hardware. Experiments demonstrate that paid OpenAI models can produce optimized Kokkos code for only a few dollars, outperforming Fortran baselines, while open‑source models often fail to generate functional code.
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arXiv Machine Learning
Jun 3
KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
arXiv:2606. 02963v1 Announce Type: new Abstract: Production inference increasingly targets a heterogeneous mix of accelerators.
arXiv AI
Jun 4
CodegenBench: Can LLMs Write Efficient Code Across Architectures?
arXiv:2606. 04023v1 Announce Type: cross Abstract: While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.
arXiv AI
Jun 9
Structuring agentic AI for HPC code modernization
arXiv:2606. 08710v1 Announce Type: cross Abstract: Modernization of legacy scientific codes is often necessary to keep up with the ever-evolving changes in the compute resource ecosystem.
arXiv AI
Aug 28
Exploring the Role of LLMs in HPC Programming: A Survey
The survey reviews how Large Language Models (LLMs) are being used in High‑Performance Computing (HPC) programming, covering code generation, parallelization, frameworks, evaluation, and broader challenges. It finds that general‑purpose LLMs perform adequately on serial and OpenMP‑style tasks but struggle with distributed MPI workloads, while domain‑specialized models achieve higher accuracy yet are limited in scope and evaluation. The authors argue that LLMs will not replace HPC experts soon but can act as powerful collaborators, provided richer datasets, integration with performance tools, rigorous evaluation, and governance are developed.
arXiv AI
Aug 10
HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation
arXiv:2608. 06791v1 Announce Type: cross Abstract: Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring months of specialized effort.
Hugging Face Trending Papers
Aug 7
HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation
Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring months of specialized effort. Even with high-level synthesis (HLS), designers still need extensive hardware expertise to build high-performance accelerators.
arXiv AI
Sep 7
MaxKernel: Agentic Kernel Generation for TPUs
MaxKernel is a multi‑agent system designed to generate high‑performance custom kernels for TPUs. It offers three paradigms: a Human‑in‑the‑Loop agent for collaborative design, an Autonomous agent that runs a fully automated optimization loop, and a Graph‑Based Autonomous Search for global exploration. All paradigms share specialized sub‑agents for planning, implementation, debugging, testing, and profiling, and the system consistently matches expert hand‑tuned baselines on the JaxBench suite and real‑world workloads.
arXiv AI
Sep 30
AI as a Compiler: Compiling Triton kernels without the Triton compiler
The paper explores using large language models (LLMs) to replace traditional compiler backends, a process termed AI lowering. An LLM agent translates Triton kernels directly into NVIDIA PTX, achieving 0.83x–3.34x the performance of autotuned Triton on a variety of GPUs and ML kernels. The study also extends a PTX verifier to support modern GPU features, highlighting the potential for AI compilers to reduce engineering effort for new hardware.
arXiv Machine Learning
Aug 6
stratum: A System Infrastructure for Massive Agent-Centric ML Workloads
arXiv:2603. 03589v3 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated.
arXiv AI
Jul 28
ParBench: A Benchmark for Reliable Evaluation of LLM Parallel Code Translation
arXiv:2607. 22588v1 Announce Type: new Abstract: Modern compute-intensive software must migrate across a changing ecosystem of accelerators, programming APIs, compiler stacks, and portability layers, including CUDA, OpenMP, OpenCL, and OpenMP target offload.
arXiv Computation and Language
Sep 7
JLIR: A Julia-Native MLIR-Inspired Intermediate Representation with Automatic JACC Kernel Extraction
JLIR is a Julia-native intermediate representation inspired by MLIR that enables multi-level, dialect-oriented compilation within the Julia ecosystem. It allows Julia programs to be represented before low-level lowering, supports extensible operations and transformation passes via Julia’s language mechanisms, and keeps partially typed programs transformable until concrete types are known. The framework includes built‑in dialects for arithmetic, control flow, functions, structured loops, and memory operations, and can be extended with new domain operations without altering the core system. JLIR was demonstrated by automatically generating JACC kernels for accelerators.