high performance computing on graphics processing units: hgpu.org

The efficiency of GPU kernels is central to the progress of modern AI, yet optimizing them remains a difficult and labor-intensive task due to complex interactions between memory hierarchies, thread scheduling, and hardware-specific characteristics. While recent advances in large language models (LLMs) provide new opportunities for automated code generation, existing approaches largely treat LLMs as […]

Tensor Cores (TCs) are specialized hardware units designed for efficient matrix multiplication and are widely utilized in deep learning workloads. However, their adoption in more irregular high-performance computing (HPC) applications remains limited. This paper presents a methodology for effectively integrating TCs into a representative HPC application: molecular docking with AutoDockGPU. The irregular computational patterns and […]

Recent leaps in large language models (LLMs) caused a revolution in programming tools (like GitHub Copilot) that can help with code generation, debugging, and even performance optimization. In this paper, we focus on the capabilities of the most recent reasoning models to generate optimized CUDA code for predefined, well-known tasks. Our objective is to determine […]

Generative AI (GenAI) workloads have rapidly become the predominant data center GPU workload. However, designing efficient GPU kernels for GenAI presents significant challenges due to two central factors: (1) GenAI workloads are intrinsically dynamic—featuring variable sequence lengths and irregular sparsity patterns—and (2) they evolve at a rapid pace, with shifting model architectures and changing deployment […]

Large-scale network sensing plays a vital role in network traffic analysis and characterization. As network packet data grows increasingly large, parallel methods have become mainstream for network analytics. While effective, GPU-based implementations still face start-up challenges in host-device memory management and porting complex workloads on devices, among others. To mitigate these challenges, composable frameworks have […]

Specializing kernels by including runtime information during just-in-time (JIT) -compilation can improve performance at the expense of potentially generating more kernels. In this work, we contribute the runtime adaptivity framework that we have implemented in AdaptiveCpp. This framework can automatically generate specialized kernels at JIT-time, automatically taking into account various information about the kernel invocation, […]

Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as attention mechanisms. The paper introduces Neptune, a tensor compiler for advanced operator fusion for sequences […]

We extend the GenTen tensor decomposition package by introducing an accelerated dense matricized tensor times Khatri-Rao product (MTTKRP), the workhorse kernel for canonical polyadic (CP) tensor decompositions, that is portable and performant on modern CPU and GPU architectures. In contrast to the state-of-the-art matrix multiply based MTTKRP kernels used by Tensor Toolbox, TensorLy, etc., that […]

In this work we present the porting to Graphics Processing Units (GPUs, using OpenMP target directives) and optimization of a key module within the cosmological {pinocchio} code, a Lagrangian Perturbation Theory (LPT)-based framework widely used for generating dark matter (DM) halo catalogs. Our optimization focuses on a specific segment of the code responsible for calculating […]

GPU kernel generation by LLMs has recently experienced rapid development, leveraging test-time scaling and reinforcement learning techniques. However, a key challenge for kernel generation is the scarcity of high-quality data, as most high-quality kernels are proprietary and not open-source. This challenge prevents us from leveraging supervised fine-tuning to align LLMs to the kernel generation task. […]

MLIR (Multi-Level Intermediate Representation) has rapidly become a foundational technology for modern compiler frameworks, enabling extensibility across diverse domains. However, ensuring the correctness and robustness of MLIR itself remains challenging. Existing fuzzing approaches-based on manually crafted templates or rule-based mutations-struggle to generate sufficiently diverse and semantically valid test cases, making it difficult to expose subtle […]

The digital revolution of our society is driven by major technological advancements, enabled not only by the growing capabilities of computers but also by the evolution of their uses. These developments result from a complex interaction between what we can do, what we know how to do, and what we want to do, all within […]