high performance computing on graphics processing units: hgpu.org

We introduce CASS, the first large-scale dataset and model suite for cross-architecture GPU code transpilation, targeting both source-level (CUDA<->HIP) and assembly-level (Nvidia SASS<->AMD RDNA3) translation. The dataset comprises 70k verified code pairs across host and device, addressing a critical gap in low-level GPU code portability. Leveraging this resource, we train the CASS family of domain-specific […]

Scheduling All-to-All communications efficiently is fundamental to minimizing job completion times in distributed systems. Incast and straggler flows can slow down All-to-All transfers; and GPU clusters bring additional straggler challenges due to highly heterogeneous link capacities between technologies like NVLink and Ethernet. Existing schedulers all suffer high overheads relative to theoretically optimal transfers. Classical, simple […]

Smartphone users often replace their devices prematurely for newer models, contributing to the growing issue of waste electrical and electronic equipment (WEEE). Repurposing these devices to extend their life cycle by assigning them new roles can help mitigate this problem. This thesis explores the feasibility of creating a cluster using upcycled smartphones deployed with the […]

Graph embeddings provide continuous vector representations of nodes in a graph, which are widely applicable in community detection, recommendations, and various scientific fields. However, existing graph embedding systems either face scalability challenges due to the high cost of RAM and multiple GPUs, or rely on disk storage at the expense of I/O efficiency. In this […]

Accurate determination of the performance of parallel GPU code typically requires execution-time profiling on target hardware — an increasingly prohibitive step due to limited access to high-end GPUs. This paper explores whether Large Language Models (LLMs) can offer an alternative approach for GPU performance prediction without relying on hardware. We frame the problem as a […]

Parallel functional array languages are an emerging class of programming languages that promise to combine low-effort parallel programming with good performance and performance portability. We systematically compare the designs and implementations of five different functional array languages: Accelerate, APL, DaCe, Futhark, and SaC. We demonstrate the expressiveness of functional array programming by means of four […]

As LLMs grow in complexity, achieving state-of-the-art performance requires tight co-design across algorithms, software, and hardware. Today’s reliance on a single dominant platform limits portability, creates vendor lock-in, and raises barriers for new AI hardware. In this work, we make the case for combining just-in-time (JIT) compilation with kernel parameter autotuning to enable portable, state-of-the-art […]

This study systematically tests a computational power reuse scheme proposed by the open source community disabling specific instruction sets (Fused Multiply Add instructions) through CUDA source code modifications on the NVIDIA CMP 170HX platform. Experimental results validate the effectiveness of this approach, partially restoring the GPU’s computational capabilities in artificial intelligence (AI) tasks. Performance evaluations […]

FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising the abstraction level from RTL to untimed C/C++, yet attaining high performance still demands expert knowledge and iterative manual insertion of optimization pragmas to modify the microarchitecture. To address this challenge, we […]

Real-time visualization of computational simulations running over graphics processing units (GPU) is a valuable feature in modern science and technological research, as it allows researchers to visually assess the quality and correctness of their computational models during the simulation. Due to the high throughput involved in GPU-based simulations, classical visualization approaches such as ones based […]

Driven by the advancements in generative AI, large machine learning models have revolutionized domains such as image processing, audio synthesis, and speech recognition. While server-based deployments remain the locus of peak performance, the imperative for on-device inference, necessitated by privacy and efficiency considerations, persists. Recognizing GPUs as the on-device ML accelerator with the widest reach, […]

Deep Learning (DL) has become a cornerstone of modern Artificial Intelligence (AI), powering applications across healthcare, computer vision, and autonomous systems. However, executing DL inference on resource-constrained end devices—such as smartphones and IoT hardware—poses challenges due to limited computational resources, energy constraints, and real-time requirements. This thesis addresses the optimization of DL inference on Heterogeneous […]