high performance computing on graphics processing units: hgpu.org

Providing parallel programming education is an emerging challenge, requires teaching approaches to further the learning process and a complex infrastructure to provide a suitable environment for the laboratory practical classes. Do not prioritize parallel programming requirements in future computing professionals learning can lead to a significant training gap, negatively impacting the efficient use of current […]

Data analysts predict that the GPU as a Service (GPUaaS) market will grow from US$700 million in 2019 to $7 billion in 2025 with a compound annual growth rate of over 38% to support 3D models, animated video processing, and gaming. GPUaaS adoption will be also boosted by the use of graphics processing units (GPUs) […]

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to ubiquitous graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging because of the gap between the difficulty in developing efficient FPGA accelerators and the […]

In a general graph data structure like an adjacency matrix, when edges are homogeneous, the connectivity of two nodes can be sufficiently represented using a single bit. This insight has, however, not yet been adequately exploited by the existing matrix-centric graph processing frameworks. This work fills the void by systematically exploring the bit-level representation of […]

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, […]

We introduce the R package clrng which leverages the gpuR package and is able to generate random numbers in parallel on a Graphics Processing Unit (GPU) with the clRNG (OpenCL) library. Parallel processing with GPU’s can speed up computationally intensive tasks, which when combined with R, it can largely improve R’s downsides in terms of […]

Auto-scheduling is a process where a search algorithm automatically explores candidate schedules (program transformations) for a given tensor program on a given hardware platform to improve its performance. However this can be a very time consuming process, depending on the complexity of the tensor program, and capacity of the target device, with often many thousands […]

GPU (Graphics Processing Unit) computing is one of the most popular accelerating methods for various high-performance computing applications. For scientific computations based on multi-physical phenomena, however, a single device solution on a GPU is insufficient, where the single timescale or degree of parallelism is not simply supported by a simple GPU-only solution. We have been […]

Parametric and non-parametric machine learning potentials have emerged recently as a way to improve the accuracy of bio-molecular simulations. Here, we present NNP/MM, an hybrid method integrating neural network potentials (NNPs) and molecular mechanics (MM). It allows to simulate a part of molecular system with NNP, while the rest is simulated with MM for efficiency. […]

We devise a performance model for GPU training of Deep Learning Recommendation Models (DLRM), whose GPU utilization is low compared to other well-optimized CV and NLP models. We show that both the device active time (the sum of kernel runtimes) and the device idle time are important components of the overall device time. We therefore […]

Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are active. The combination of active CPU and GPU cores achieving the maximum performance depends on a workload’s characteristics, making manual […]

Graphics Processing Units (GPUs) have evolved from very specialized designs geared towards computer graphics to accommodate general-purpose highly-parallel workloads. Harnessing the performance that these accelerators provide requires the use of specialized native programming interfaces, such as CUDA or OpenCL, or higher-level programming models like OpenMP or OpenACC. However, on managed programming languages, offloading execution into […]