activity
20202026
most citedEvaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

42 citations · 96 across the 12 of their papers we have counts for

collaborators

13 papers

cs.LG2026

Product-Aware Deterministic Rounding for Quantized Matrix Multiplication

Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2

Scalar rounding decisions interact through matrix multiplication. We study deterministic product-aware rounding after scales, clipping bounds, and grids are fixed, with each active…

cs.IT2026

A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Products

Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2

We consider the problem of minimizing error in quantized matrix multiplication . Scalar quantization of the factors introduces rounding errors whose scale depends on the maxi…

math.NA2026

A Second-Moment Theory for Floating-Point Reduction Trees

Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2

Summation error depends on partial-sum order, which standard worst-case bounds omit. To capture this dependence, we derive an exact mean-square error (MSE) recurrence for a binary…

cs.LG2026

Contraction-Gauge Preconditioning for Quantized Matrix Multiplication

Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2

We study low-precision computation of C=AB with both factors quantized. We derive an exact finite-dimensional identity for the expected squared product error under independent, zer…

cs.DC2025★ 2 cited

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

William F. Godoy, Tatiana Melnichenko, Pedro Valero-Lara +5

We explore the performance and portability of the novel Mojo language for scientific computing workloads on GPUs. As the first language based on the LLVM's Multi-Level Intermediate…

cs.SE2025★ 2 cited

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions

Keita Teranishi, Harshitha Menon, William F. Godoy +25

We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular…

cs.PF2025★ 3 cited

Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications

William F. Godoy, Oscar Hernandez, Paul R. C. Kent +8

We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo pac…

cs.DC2023★ 7 cited

Julia as a unifying end-to-end workflow language on the Frontier exascale system

William F. Godoy, Pedro Valero-Lara, Caira Anderson +4

We evaluate Julia as a single language and ecosystem paradigm powered by LLVM to develop workflow components for high-performance computing. We run a Gray-Scott, 2-variable diffusi…

cs.SE2023★ 5 cited

Comparing Llama-2 and GPT-3 LLMs for HPC kernels generation

Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail +4

We evaluate the use of the open-source Llama-2 model for generating well-known, high-performance computing kernels (e.g., AXPY, GEMV, GEMM) on different parallel programming models…

cs.AI2023★ 42 cited

Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation

William F. Godoy, Pedro Valero-Lara, Keita Teranishi +2

We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG. We t…

cs.DC2023★ 21 cited

Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes

William F. Godoy, Pedro Valero-Lara, T. Elise Dettling +7

We explore the performance and portability of the high-level programming models: the LLVM-based Julia and Python/Numba, and Kokkos on high-performance computing (HPC) nodes: AMD Ep…

cs.DC2021★ 2 cited

cuConv: A CUDA Implementation of Convolution for CNN Inference

Marc Jordà, Pedro Valero-Lara, Antonio J. Peña

Convolutions are the core operation of deep learning applications based on Convolutional Neural Networks (CNNs). Current GPU architectures are highly efficient for training and dep…

cs.DC2020★ 12 cited

MPI+OpenMP Tasking Scalability for Multi-Morphology Simulations of the Human Brain

Pedro Valero-Lara, Raül Sirvent, Antonio J. Peña +1

The simulation of the behavior of the human brain is one of the most ambitious challenges today with a non-end of important applications. We can find many different initiatives in…