high performance computing on graphics processing units: hgpu.org

GPUs offer massive compute parallelism and high-bandwidth memory accesses. GPU database systems seek to exploit those capabilities to accelerate data analytics. Although modern GPUs have more resources (e.g., higher DRAM bandwidth) than ever before, judicious choices for query processing that avoid wasteful resource allocations are still advantageous. Database systems can save GPU runtime costs through […]

Various kinds of applications take advantage of GPUs through automation tools that attempt to automatically exploit the available performance of the GPU’s parallel architecture. Directive-based programming models, such as OpenACC, are one such method that easily enables parallel computing by just adhering code annotations to code loops. Such abstract models, however, often prevent programmers from […]

The Square Kilometre Array (SKA) is the world’s largest radio telescope currently under construction, and will employ elaborate signal processing to detect new pulsars, i.e. highly magnetised rotating neutron stars. This paper addresses the acceleration of demanding computations for this pulsar search on Field-Programmable Gate Arrays (FPGAs) using a new high-level design process based on […]

Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern […]

On-device machine learning (ML) inference can enable the use of private user data on user devices without remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. To overcome this barrier, we propose the use of […]

Motion Estimation is one of the main tasks behind any video encoder. It is a computationally costly task; therefore, it is usually delegated to specific or reconfigurable hardware, such as FPGAs. Over the years, multiple FPGA implementations have been developed, mainly using hardware description languages such as Verilog or VHDL. Since programming using hardware description […]

A merge tree is a topological descriptor of a real-valued function. Merge trees are used in visualization and topological data analysis, either directly or as a means to another end: computing a 0-dimensional persistence diagram, identifying connected components, performing topological simplification, etc. Scientific computing relies more and more on GPUs to achieve fast, scalable computation. […]

OpenCL functions as a portability layer for diverse heterogeneous hardware platforms including CPUs, GPUs, FPGAs, and hardware accelerators. However, OpenCL programs utilizing multiple of these devices in the same computing platform suffer from poor coordination between OpenCL implementations of different hardware vendors. This paper proposes a vendor-independent open source method for integrating custom FPGA accelerators […]

The resource demands of HPC applications vary significantly. However, it is common for HPC systems to assign resources on a per-node basis to prevent interference from co-located workloads. This gap between the coarse-grained resource allocation and the varying resource demands can lead to underutilization of HPC resources. In this study, we comprehensively analyzed the resource […]

Recent advances in deep learning base on growing model sizes and the necessary scaling of compute power. Training such large-scale models requires an intricate combination of data-, operator-, and pipeline parallelism in complex distributed systems. We show how to use OneFlow’s Split, Broadcast, and Partial Sum (SBP) tensor formulations to enable new distributed training methods […]

Relational graph neural networks (RGNNs) are graph neural networks (GNNs) with dedicated structures for modeling the different types of nodes and/or edges in heterogeneous graphs. While RGNNs have been increasingly adopted in many real-world applications due to their versatility and accuracy, they pose performance and system design challenges due to their inherent computation patterns, gap […]

Molecular dynamics simulations are a core element of research in physics, chemistry and biology. A key aspect for extending the capability of simulation tools is providing access to advanced sampling methods and techniques that permit calculation of the relevant, underlying free energy landscapes. In this sense, software tools that can be seamlessly adapted to a […]