high performance computing on graphics processing units: hgpu.org

As multicore vector processors improve in computational and memory performance, running SIMT (Single Instruction Multiple Threads) programs on CPUs has become increasingly appealing, potentially eliminating the need for dedicated GPU hardware. SYCL is a royalty-free cross-platform C++ programming model for heterogeneous computing that implements the SIMT model and provides a path to run GPU programs […]

In this thesis, we address the challenge of performance portability in heterogeneous computing environments. Performance portability refers to the ability of an application to maintain high performance on multiple platforms without requiring extensive manual tuning for each system. Traditional containers fall short in this regard as they prioritize portability at the expense of architecture-specific optimizations. […]

Owing to the huge success of generative artificial intelligence (AI), large language models (LLMs) have emerged as a core subclass, underpinning applications such as question answering, text generation, and code completion. While fine-tuning these models on domain-specific data can yield significant performance gains, it also poses daunting computational challenges, especially for researchers and small organizations […]

Performing binary search on a sorted dense array is a widely used baseline when benchmarking sophisticated index structures: It is simple, fast to build, and indexes the dataset with minimal memory footprint. However, the popular opinion is that it cannot compete with sophisticated indexes in terms of lookup performance, and hence, should not actually be […]

GPU computing is embracing weak memory concurrency for performance improvement. However, compared to CPUs, modern GPUs provide more fine-grained concurrency features such as scopes, have additional properties like divergence, and thereby follow different weak memory consistency models. These features and properties make concurrent programming on GPUs more complex and error-prone. To this end, we present […]

Batched kernels with memory allocations is a common pattern in HPC, appearing in multi-dimensional FFTs, neural networks processing, or split computation of numerical operators. Its efficient support is especially complex on GPU where memory per work-item is limited and dynamic memory allocations are challenging. This study investigates whether the native abstractions of SYCL can support […]

This work examines latency, throughput, and other metrics when performing inference on confidential GPUs. We explore different traffic patterns and scheduling strategies using a single Virtual Machine with one NVIDIA H100 GPU, to perform relaxed batch inferences on multiple Large Language Models (LLMs), operating under the constraint of swapping models in and out of memory, […]

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 […]

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 […]

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 […]