Domain-Specific languages (DSLs) such as Triton enable developers to write high-performance GPU kernels in a Python-friendly manner; however, profiling these kernels with existing tools often incurs runtime and storage overhead while failing to deliver actionable insights for both kernel authors and framework developers. We present Proton, a multi-level, adaptive profiler tailored for the Triton programming language and compiler. Proton provides frontend APIs to selectively profile relevant regions, aggregate results, capture custom metrics not available through hardware counters, and query profiles using a SQL-like language. Proton’s backend design unifies vendor profiling APIs with instrumentation-based profiling, ensuring portability and extensibility. Using Proton, users are able to query custom and hardware metrics across the relevant levels of abstraction—full end-to-end model execution, isolated neural network layers, language-specific Triton operators, and compiler intermediate representations. We demonstrate the tool’s effectiveness through case studies on production-grade kernel development, continuous integration, multi-GPU analysis, language model inference, and intra-kernel profiling. Our evaluations on end-to-end workloads, as well as standalone Triton kernels, demonstrate that Proton imposes lower runtime overhead and delivers significant reductions in profile sizes relative to existing framework and vendor profilers while being fully open source.

We present HF-ICA, a second-order “Hessian-free” algorithm for Infomax-ICA. Our approach achieves asymptotically quadratic convergence while retaining the memory footprint of first-order methods. Without any hyperparameter tuning, we show better convergence properties than both other approximate Newton-type methods and finely-tuned stochastic Natural Gradient Descent on EEG and fMRI data. A portable, multi-threaded and vectorized C++ implementation is made publicly available along with MATLAB and Python interfaces.

Efficient implementations of HPC applications for parallel architectures generally rely on external software packages (e.g., BLAS, LAPACK, CUDNN). While these libraries provide highly optimized routines for certain characteristics of inputs (e.g., square matrices), they generally do not retain optimal performance across the wide range of problems encountered in practice. In this paper, we present an input-aware auto-tuning framework for matrix multiplications and convolutions, ISAAC, which uses predictive modeling techniques to drive highly parameterized PTX code templates towards not only hardware-, but also application-specific kernels. Numerical experiments on the NVIDIA Maxwell and Pascal architectures show up to 3x performance gains over both cuBLAS and cuDNN after only a few hours of auto-tuning.