high performance computing on graphics processing units: hgpu.org

FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading FPGA vendors have enhanced their architectures to more efficiently support the computational demands of DL workloads. However, the two most prominent AI-optimized FPGAs, i.e., AMD/Xilinx Versal ACAP and Intel Stratix 10 […]

In the context of the Edge-Cloud computing continuum, containerization and orchestration have become two key requirements in software development best practices. Containerization allows for better resource utilization, platform-independent development, and secure software deployment. Orchestration automates the deployment, networking, scaling, and availability of containerized workloads and services. However, there are still several open challenges. First, the […]

The open SYCL standard has established itself as a cross-vendor, cross-platform means to develop software which benefits from GPU and accelerator parallelism. Inherent difficulties in portability between and debuggability of programs for these targets remain. However, as we demonstrate, the SYCL specification lends itself to be implemented purely in software in a manner that is […]

To meet the increasing demand of quantum chemistry calculations in data-driven chemical research, the collaboration between industrial stakeholders and the quantum chemistry community has led to the development of GPU4PySCF, a GPU-accelerated Python package. This open-source project is accessible via its public GitHub repository. This paper outlines the primary features, innovations, and advantages of this […]

With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep learning, the large-scale scenario poses new challenges that include fault tolerance, scalability of algorithms and infrastructures, and heterogeneity in data sets, […]

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread applications in iterative algorithms. Over the past decades, research on SpMV optimization has made remarkable strides, giving rise to various optimization contributions. However, the comprehensive and systematic literature survey that introduces, analyzes, discusses, and summarizes the advancements of SpMV in recent years is currently […]

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model […]

Reducing the need for users to manually manage the details of work and data distribution is an important goal of high-level many-task runtime systems. For distributed memory platforms this means that the runtime system has to keep track of both fine-grained task dependencies and data residency meta-information. The amount of such meta-information is proportional to […]

Nearly 20 years after the birth of general purpose GPU computing, the HPC landscape is now dominated by GPUs. After years of undisputed dominance by NVIDIA, new players have entered the arena in a convincing manner, namely AMD and more recently Intel, whose devices currently power the first two clusters in the Top500 ranking. Unfortunately, […]

Artificial intelligence (AI) depends on data. In sensitive domains – such as healthcare, security, finance, and many more – there is therefore tension between unleashing the power of AI and maintaining the confidentiality and security of the relevant data. This book – intended for researchers in academia and R&D engineers in industry – explains how […]

CIFAR-10 is among the most widely used datasets in machine learning, facilitating thousands of research projects per year. To accelerate research and reduce the cost of experiments, we introduce training methods for CIFAR-10 which reach 94% accuracy in 3.29 seconds, 95% in 10.4 seconds, and 96% in 46.3 seconds, when run on a single NVIDIA […]

Determining the maximum usage of random-access memory (RAM) on both the motherboard and on a graphical processing unit (GPU) over the lifetime of a computing task can be extremely useful for troubleshooting points of failure as well as optimizing memory utilization, especially within a high-performance computing (HPC) setting. While there are tools for tracking compute […]