high performance computing on graphics processing units: hgpu.org

Nowadays a wide range of applications is constrained by low-latency requirements that cloud infrastructures cannot meet. Multi-access Edge Computing (MEC) has been proposed as the reference architecture for executing applications closer to users and reduce latency, but new challenges arise: edge nodes are resource-constrained, the workload can vary significantly since users are nomadic, and task […]

We explore the design and development of structured-mesh based solvers on current Intel FPGA hardware using the SYCL programming model. Two classes of applications are targeted : (1) stencil applications based on explicit numerical methods and (2) multidimensional tridiagonal solvers based on implicit methods. Both classes of solvers appear as core modules in a wide-range […]

We describe the experience of converting a CUDA implementation of a parallel graph coloring algorithm to SYCL. The goals are for our work to be useful to application and compiler developers by providing a detailed description of migration paths between CUDA and SYCL. We will describe how CUDA functions are mapped to SYCL functions. Evaluating […]

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggregated and […]

Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS). To achieve this goal, we build a distributed NAS system to search […]

Automatic code generation is frequently used to create implementations of algorithms specifically tuned to particular hardware and application parameters. The code generation process involves the selection of adequate code transformations, tuning parameters, and parallelization strategies. We propose an alternative to time-intensive autotuning, scenario-specific performance models, or black-box machine learning to select the best-performing configuration. This […]

Providing convenient APIs and notations for data parallelism which remain accessible for programmers while still providing good performance has been a long-term goal of researchers as well as language and library designers. C++20 introduces ranges and views, as well as the composition of operations on them using a concise syntax, but the efficient implementation of […]

Modern computing platforms tend to deploy multiple GPUs on a single node to boost performance. GPUs have large computing capacities and are an expensive resource. Increasing their utilization without causing performance degradation of individual workloads is an important and challenging problem. Although services such as NVIDIA’s MPS allow multiple cooperative kernels to simultaneously run on […]

To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the complexity of high-level programs down to the universal intermediate representation, extracting the specific computational patterns and predicting which code segments would run best on a specific core […]

The wide adoption of SYCL as an open-standard API for accelerating C++ software in domains such as HPC, Automotive, Artificial Intelligence, Machine Learning, and other areas necessitates efficient compiler and runtime support for a growing number of different platforms. Existing SYCL implementations provide support for various devices like CPUs, GPUs, DSPs, FPGAs, etc, typically via […]

OpenMP is the preferred choice for CPU parallelism in High-Performance-Computing (HPC) applications written in C, C++, or Fortran. As HPC systems became heterogeneous, OpenMP introduced support for accelerator offloading via the target directive. This allowed porting existing (CPU) code onto GPUs, including well established CPU parallelism paradigms. However, there are architectural differences between CPU and […]

Parallel programming is increasingly used to improve the performance of solving numerical methods used for scientific purposes. Numerical methods in the field of fluid dynamics require the calculation of a large number of operations per second. One of the methods that is easily parallelized and often used is the Lattice Boltzmann method (LBM). Today, it […]