high performance computing on graphics processing units: hgpu.org

Differential evolution (DE) is an effective global evolutionary optimization algorithm using to solve global optimization problems mainly in a continuous domain. In this field, researchers pay more attention to improving the capability of DE to find better global solutions, however, the computational performance of DE is also a very interesting aspect especially when the problem […]

Popular network pruning algorithms reduce redundant information by optimizing hand-crafted parametric models, and may cause suboptimal performance and long time in selecting filters. We innovatively introduce non-parametric modeling to simplify the algorithm design, resulting in an automatic and efficient pruning approach called EPruner. Inspired by the face recognition community, we use a message passing algorithm […]

We propose a new framework for computing the embeddings of large-scale graphs on a single machine. A graph embedding is a fixed length vector representation for each node (and/or edge-type) in a graph and has emerged as the de-facto approach to apply modern machine learning on graphs. We identify that current systems for learning the […]

Spatial computing devices have been shown to significantly accelerate stencil computations, but have so far relied on unrolling the iterative dimension of a single stencil operation to increase temporal locality. This work considers the general case of mapping directed acyclic graphs of heterogeneous stencil computations to spatial computing systems, assuming large input programs without an […]

High performance computing (HPC) is now well established as the cornerstone for building and conducting software simulations in numerous scientific and industrial fields. The hardware complexity of supercomputers is steadily increasing, however, to deliver ever improved computing performance, causing the complexity of HPC application development to increase as well. As a result, the need for […]

In the world of algorithm acceleration and the implementation of deep neural networks’ recall phase, OpenCL based solutions have a clear tendency to produce perfectly adapted kernels in graphic processor unit (GPU) architectures. However, they fail to obtain the same results when applied to field-programmable gate array (FPGA) based architectures. This situation, along with an […]

For the last years, we observe the intensive development of accelerated computing platforms. Although current trends indicate a well-established position of GPU devices in the HPC environment, FPGA (Field-Programmable Gate Array) aspires to be an alternative solution to offload the CPU computation. This paper presents a systematic adaptation of four various CFD (Computational Fluids Dynamic) […]

In e-commerce industry, user behavior sequence data has been widely used in many business units such as search and merchandising to improve their products. However, it is rarely used in financial services not only due to its 3V characteristics – i.e. Volume, Velocity and Variety – but also due to its unstructured nature. In this […]

We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with large convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate how to achieve inference latency of 5μs using convolutional architectures, while preserving state-of-the-art model performance. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various […]

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data is often a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise package that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed […]

This doctoral thesis covers some of my advances in electron microscopy with deep learning. Highlights include a comprehensive review of deep learning in electron microscopy; large new electron microscopy datasets for machine learning, dataset search engines based on variational autoencoders, and automatic data clustering by t-distributed stochastic neighbour embedding; adaptive learning rate clipping to stabilize […]

Stencil codes (a.k.a. nearest-neighbor computations) are widely used in image processing, machine learning, and scientific applications. Stencil codes incur nearest-neighbor data exchange because the value of each point in the structured grid is calculated as a function of its value and the values of a subset of its nearest-neighbor points. When running on Graphics Processing […]