high performance computing on graphics processing units: hgpu.org

Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL libraries with highly-specialized kernels for each workload/architecture, leading to numerous, complex code-bases that strive for performance, yet they are hard to maintain and do not generalize. In […]

Heterogeneous processors integrate very distinct compute resources such as CPUs and GPUs into the same chip, thus can exploit the advantages and avoid disadvantages of those compute units. We in this work evaluate and analyze eight sparse matrix and graph kernels on an AMD CPU-GPU heterogeneous processor by using 956 sparse matrices. Five characteristics, i.e., […]

We explore a first proof-of-concept example of creatively using the Turing generation’s hardware ray tracing cores to solve a problem other than classical ray tracing, specifically, point location in unstructured tetrahedral meshes. Starting with a CUDA reference method, we describe and evaluate three different approaches to reformulate this problem in a manner that allows it […]

Multigrid methods are fast and scalable numerical solvers for partial differential equations (PDEs) that possess a large design space for implementing their algorithmic components. Code generation approaches allow formulating multigrid methods on a higher level of abstraction that can then be used to define a problem- and hardwarespecific solution. Since these problems have considerable implementation […]

The increasing demands of modern embedded systems, such as highperformance and energy-efficiency, have motivated the use of heterogeneous multicore platforms enabled by Multiprocessor System-on-Chips(MPSoCs). To fully exploit the power of these platforms, new tools are needed to address the increasing software complexity to achieve a high productivity. An MPSoC compiler is a toolchain to tackle […]

Hash aggregation is an important data processing primitive which can be significantly accelerated by modern graphics processors (GPUs). Previous work derived heuristics for GPU-accelerated hash aggregation from the study of a particular GPU. In this paper, we examine the influence of different execution parameters on GPUaccelerated hash aggregation on four NVIDIA and two AMD GPUs […]

Monte Carlo methods are core to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional state spaces where they are still a method of choice in the financial industry. Recently, Tensor Processing Units (TPUs) have […]

Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional […]

Neuronal networks are often modeled as systems of coupled, nonlinear, ordinary or partial differential equations. The number of differential equations used to model a network increases with the size of the network and the level of detail used to model individual neurons and synapses. As one scales up the size of the simulation it becomes […]

Discrete Event Simulation (DES) is widely used for analysis of complex temporospatial epidemic models. In such simulations, a conspicuous fraction (50%-90%) of simulation runtime is typically spent in solving equations used to model epidemic progression. General Purpose Graphics Processing Units (GPGPUs) hold considerable potential to reduce time for solving epidemic equations. However, the significant differences […]

Intel’s Xeon Phi combines the parallel processing power of a many-core accelerator with the programming ease of CPUs. In this paper, we present a survey of works that study the architecture of Phi and use it as an accelerator for a broad range of applications. We review performance optimization strategies as well as the factors […]

OpenCL offers code portability but no performance portability. Given an OpenCL program X specifically written for one platform P, existing OpenCL compilers, which usually optimize its host and kernel codes individually, often yield poor performance for another platform Q. Instead of obtaining a performance-improved version of X for Q via manual tuning, we aim to […]