high performance computing on graphics processing units: hgpu.org

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and […]

Currently, the Weather Research and Forecasting model (WRF) utilizes shared memory (OpenMP) and distributed memory (MPI) parallelisms. To take advantage of GPU resources on the Perlmutter supercomputer at NERSC, we port parts of the computationally expensive routines of the Fast Spectral Bin Microphysics (FSBM) microphysical scheme to NVIDIA GPUs using OpenMP device offloading directives. To […]

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are […]

The rapid advancement of Artificial Intelligence (AI) necessitates significant enhancements in the energy efficiency of Graphics Processing Units (GPUs) for Deep Neural Network (DNN) workloads. Such a challenge is particularly critical for embedded GPUs, which operate within stringent power constraints. Traditional GPU architectures, designed to support a limited set of numeric formats, face challenges in […]

Since the advent of parallel algorithms in the C++17 Standard Template Library (STL), the STL has become a viable framework for creating performance-portable applications. Given multiple existing implementations of the parallel algorithms, a systematic, quantitative performance comparison is essential for choosing the appropriate implementation for a particular hardware configuration. In this work, we introduce a […]

GPUs are commonly used to accelerate the execution of applications in domains such as deep learning. Deep learning applications are applied to an increasing variety of scenarios, with edge computing being one of them. However, edge devices present severe computing power and energy limitations. In this context, the use of remote GPU virtualization solutions is […]

Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networks, with bandwidths up to a few terabits per second. However, gauging performance expectations and maximizing system efficiency is challenging due to different technologies, design options, and software layers. This […]

Many computational problems consider memory throughput a performance bottleneck, especially in the domain of parallel computing. Software needs to be attuned to hardware features like cache architectures or concurrent memory banks to reach a decent level of performance efficiency. This can be achieved by selecting the right memory layouts for data structures or changing the […]

Proper representation of data in graphical visualizations becomes challenging when high accuracy in data types is required, especially in those situations where the difference between double-precision floating-point and single-precision floating-point values makes a significant difference. Some of the limitations of using single-precision over double-precision include lesser accuracy, which accumulates errors over time, and poor modeling […]

In recent years, the widespread informatization and rapid data explosion have increased the demand for high-performance heterogeneous systems that integrate multiple computing cores such as CPUs, Graphics Processing Units (GPUs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and Neural Processing Units (NPUs). The combination of CPU and GPU is particularly popular due […]

Software developers must adapt to keep up with the changing capabilities of platforms so that they can utilize the power of High- Performance Computers (HPC), including exascale systems. OpenMP, a directive-based parallel programming model, allows developers to include directives to existing C, C++, or Fortran code to allow node level parallelism without compromising performance. This […]

Over the last two decades, parallelism has become the primary method for speeding up computer programs. When writing parallel code, it is often necessary to use synchronization primitives (e.g., atomics, barriers, or critical sections) to enforce correctness. However, the performance of synchronization primitives depends on a variety of complex factors that non-experts may be unaware […]