high performance computing on graphics processing units: hgpu.org

There has been considerable interest in leveraging GPUs’ computational power and high memory bandwidth for analytical database workloads. However, their limited memory capacity remains a fundamental limitation for databases whose sizes far exceed the GPU memory size. This challenge is exacerbated by the slow PCIe data transfer speed, that creates a bottleneck in overall system […]

Compiler backends are tasked with generating executable machine code for various processors. As the diversity of processors continues to grow, it is imperative for programmers to tailor specific compiler backends to accommodate each one. However, compiler backend development remains a labor-intensive and time-consuming process, with limited automation tools available. Although large language models (LLMs) have […]

The proliferation of IoT devices and advancements in network technologies have intensified the demand for real-time data processing at the network edge. To address these demands, low-power AI accelerators, particularly GPUs, are increasingly deployed for inference tasks, enabling efficient computation while mitigating cloud-based systems’ latency and bandwidth limitations. Despite their growing deployment, GPUs remain underutilised […]

GPU singletasking is becoming increasingly inefficient and unsustainable as hardware capabilities grow and workloads diversify. We are now at an inflection point where GPUs must embrace multitasking, much like CPUs did decades ago, to meet the demands of modern AI workloads. In this work, we highlight the key requirements for GPU multitasking, examine prior efforts, […]

We introduce the Bandicoot C++ library for linear algebra and scientific computing on GPUs, overviewing its user interface and performance characteristics, as well as the technical details of its internal design. Bandicoot is the GPU-enabled counterpart to the well-known Armadillo C++ linear algebra library, aiming to allow users to take advantage of GPU-accelerated computation for […]

The programming security of Compute Unified Device Architecture(CUDA), NVIDIA’s parallel computing platform and programming model for Graphics Processing Unit, has always been a significant concern. On the host-side, fuzzing has been remarkably successful at uncovering various software bugs and vulnerabilities, with hundreds of flaws discovered annually through different fuzzing tools. However, existing fuzzing tools typically […]

The ever-increasing compute performance of GPU accelerators drives up the need for efficient data movements within HPC applications to sustain performance. Proposed as a solution to alleviate CPU-GPU data movement, AMD MI300A Accelerated Processing Unit (APU) combines CPU, GPU, and high-bandwidth memory (HBM) within a single physical package. Leadership supercomputers, such as El Capitan, group […]

Recent deep learning compilers commonly adopt auto-tuning approaches that search for the optimal kernel configuration in tensor programming from scratch, requiring tens of hours per operation and neglecting crucial optimization factors for parallel computing on asymmetric multicore processors. Meanwhile, hand-optimized inference libraries from hardware vendors provide high performance but lack the flexibility and automation needed […]

Existing GPU libraries often struggle to fully exploit the parallel resources and on-chip memory (SRAM) of GPUs when chaining multiple GPU functions as individual kernels. While Kernel Fusion (KF) techniques like Horizontal Fusion (HF) and Vertical Fusion (VF) can mitigate this, current library implementations often require library developers to manually create fused kernels. Hence, library […]

This paper presents Block, a distributed scheduling framework designed to optimize load balancing and auto-provisioning across instances in large language model serving frameworks by leveraging contextual information from incoming requests. Unlike popular model serving systems that rely on monolithic and heuristic task schedulers, Block operates as a fully distributed, stateless, and predictive scheduling system to […]

This paper presents a portable, GPU-accelerated implementation of a QR-based singular value computation algorithm in Julia. The singular value ecomposition (SVD) is a fundamental numerical tool in scientific computing and machine learning, providing optimal low-rank matrix approximations. Its importance has increased even more in large-scale machine learning pipelines, including large language models (LLMs), where it […]

Rowhammer is a read disturbance vulnerability in modern DRAM that causes bit-flips, compromising security and reliability. While extensively studied on Intel and AMD CPUs with DDR and LPDDR memories, its impact on GPUs using GDDR memories, critical for emerging machine learning applications, remains unexplored. Rowhammer attacks on GPUs face unique challenges: (1) proprietary mapping of […]