high performance computing on graphics processing units: hgpu.org

Graphic Processing Units (GPUs) have become popular in the last decade due to their high memory bandwidth and powerful computing capacity. Nevertheless, achieving highperformance on GPUs is not trivial. It generally requires significant programming expertise and understanding of details of low-level execution mechanisms in GPUs. This dissertation introduces approaches for optimizing regular and irregular applications. […]

While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and […]

Solving diagonally dominant tridiagonal linear systems is a common problem in scientific high-performance computing (HPC). Furthermore, it is becoming more commonplace for HPC platforms to utilise a heterogeneous combination of computing devices. Whilst it is desirable to design faster implementations of parallel linear system solvers, power consumption concerns are increasing in priority. This work presents […]

In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space. Therefore, it has received unprecedented attention from both the academia and the business community. Compared with CPU/GPU, FPGA has attracted much attention for its high-energy efficiency, short […]

Access to vast amounts of data along with affordable computational power stimulated the reincarnation of neural networks. The progress could not be achieved without adequate software tools, lowering the entry bar for the next generations of researchers and developers. The paper introduces PyTorchPipe (PTP), a framework built on top of PyTorch. Answering the recent needs […]

CUDA Unified Memory improves the GPU programmability and also enables GPU memory oversubscription. Recently, two advanced memory features, memory advises and asynchronous prefetch, have been introduced. In this work, we evaluate the new features on two platforms that feature different CPUs, GPUs, and interconnects. We derive a benchmark suite for the experiments and stress the […]

Using high-level synthesis (HLS) tools for field-programmable gate array (FPGA) design is becoming an increasingly popular choice because HLS tools can generate a high-quality design in a short development time. However, current HLS tools still cannot adequately support users in understanding and fixing the performance issues of the current design. That is, current HLS tools […]

We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for the computationally costly sequence alignment step. The key idea of SneakySnake is to provide fast and highly accurate filtering by reducing the ASM problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR […]

Autotuning of performance-relevant source-code parameters allows to automatically tune applications without hard coding optimizations and thus helps with keeping the performance portable. In this paper, we introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA. Using our Kernel Tuning Toolkit, we show that with autotuning most of […]

The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs. The current 4th generation of mobile NPUs is already approaching the results of CUDA-compatible Nvidia graphics cards presented not long ago, which together with the increased capabilities of mobile deep learning frameworks […]

Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale. Existing parameter synchronization protocols cannot effectively leverage available network resources in the face of ever increasing hardware heterogeneity. To address this, we propose Blink, a collective communication library that dynamically generates optimal communication primitives by packing spanning trees. We propose techniques to […]

Most, if not all the modern scientific simulation packages utilize matrix algebra operations. Among the operation of the linear algebra, one of the most important kernels is the multiplication of matrices, dense and sparse. Examples of application of such a kernel are in electronic structure calculations, machine learning, data mining, graph processing, and digital signal […]