high performance computing on graphics processing units: hgpu.org

Graphics Processing Units (GPU) offer tremendous computational power by following a throughput oriented computing paradigm where many thousand computational units operate in parallel. Programming this massively parallel hardware is challenging. Programmers must correctly and efficiently coordinate thousands of threads and their accesses to various shared memory spaces. Existing mainstream GPU programming languages, such as CUDA […]

This work presents topology optimisation implementations for linear elastic compliance minimisation in three dimensions, accelerated using Graphics Processing Units (GPUs). Three different open-source implementations are presented for linear problems. Two implementations use GPU acceleration, based on either OpenMP 4.5 or the Futhark language to implement the hardware acceleration. Both GPU implementations are based on high […]

Kernel Tuning Toolkit (KTT) is an autotuning framework for CUDA, OpenCL and Vulkan kernels. KTT provides advanced autotuning features such as support for both dynamic (online) and offline tuning, and an ability to tune multiple kernels together with shared tuning parameters. Furthermore, it offers customization features that make integration into larger software suites possible. The […]

In the world of artificial intelligence (AI) at the edge, we need to focus primarily on the energy efficiency with which we approach deep neural network (DNN) applications. In many applications, the speed of obtaining an inference can be critical; but many applications easily meet their time requirements, and the energy needed to calculate the […]

Agent-Based Simulation (ABS) is a modelling approach where simulated entities i.e., agents, perform actions autonomously and interact with other agents based on a set of rules. ABSs have demonstrated their usefulness in various domains such as transportation, social science, or biology. Agent-based simulators commonly rely vastly on Central Processing Unit (CPU)-based sequential execution. As a […]

Accelerators, such as GPUs (Graphics Processing Unit) that is suitable for handling highly parallel data, and FPGA (Field Programmable Gate Array) with algorithms customized architectures, are widely adopted. The motivation is that algorithms with various parallel characteristics can efficiently map to the heterogeneous computing architecture by collaborated GPU and FPGA. However, current applications always utilize […]

Our goal in this work is to make it easy and feasible to implement solutions for autonomous decision-making and planning under uncertainty on low-power mobile platforms. We focus on practical applications, such as autonomous driving and service robotics, that must run on mobile SoC platforms. These applications often have real-time execution constraints. The main challenge […]

The ever-increasing demand for high-performance Big Data analytics and data processing has paved the way for heterogeneous hardware accelerators, such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), to be integrated into modern Big Data platforms. Currently, this integration comes at the cost of programmability, as the end-user Application Programming Interface (API) […]

Field-programmable gate arrays (FPGAs) have garnered significant interest in research on high-performance computing because their flexibility enables the building of application-specific computation pipelines and data supply systems. In addition to the flexibility, toolchains for the development of FPGAs in OpenCL have been developed and offered by FPGA vendors that reduce the programming effort required. However, […]

With the release of the Apple Silicon System-on-a-Chip processors, and the impressive performance shown in general use by both the M1 and M1 Ultra, the potential use for Apple Silicon processors in scientific computing is explored. Both the M1 and M1 Ultra are compared to current state-of-the-art data-center GPUs, including an NVIDIA V100 with PCIe, […]

The continuous technology push on the semiconductor industry has led to the development of several alternate architectures for efficient computing. Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are examples of devices used to accelerate applications. FPGAs are able to provide massive parallelism for suitable tasks when properly programmed. However, designing for FPGA is […]

Over the past few years, there has been an increased interest in including FPGAs in data centers and high-performance computing clusters along with GPUs and other accelerators. As a result, it has become increasingly important to have a unified, high-level programming interface for CPUs, GPUs and FPGAs. This has led to the development of compiler […]