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Posts

May, 23

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Although Graphics Processing Units (GPUs) have become pervasive for data-parallel workloads, the efficient exploitation of their tiered memory hierarchy requires explicit programming. The efficient utilization of different GPU memory tiers can yield higher performance at the expense of programmability since developers must have extended knowledge of the architectural details in order to utilize them. In […]
May, 23

Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to […]
May, 23

Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs

Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. However, computational science and engineering (CSE) problems require double precision accuracy in several domains. This conflict between hardware trends and application needs has resulted in a need for multiprecision strategies at […]
May, 23

CoCoNet: Co-Optimizing Computation and Communication for Distributed Machine Learning

Modern deep learning workloads run on distributed hardware and are difficult to optimize — data, model, and pipeline parallelism require a developer to thoughtfully restructure their workload around optimized computation and communication kernels in libraries such as cuBLAS and NCCL. The logical separation between computation and communication leaves performance on the table with missed optimization […]
May, 23

Comparison of HPC Architectures for Computing All-Pairs Shortest Paths. Intel Xeon Phi KNL vs NVIDIA Pascal

Today, one of the main challenges for high-performance computing systems is to improve their performance by keeping energy consumption at acceptable levels. In this context, a consolidated strategy consists of using accelerators such as GPUs or many-core Intel Xeon Phi processors. In this work, devices of the NVIDIA Pascal and Intel Xeon Phi Knights Landing […]
May, 16

NPBench: A Benchmarking Suite for High-Performance NumPy

Python, already one of the most popular languages for scientific computing, has made significant inroads in High Performance Computing (HPC). At the center of Python’s ecosystem is NumPy, an efficient implementation of the multi-dimensional array (tensor) structure, together with basic arithmetic and linear algebra. Compared to traditional HPC languages, the relatively low performance of Python […]
May, 16

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Parameter optimization is a common task in various fields such as computational biology. In these scientific fields, optimization can be, e.g. based on ordinary differential equations with the computational task getting increasingly computation-intensive for increasing complexity of ODE and the parameters to determine. Hence, this raises requirements for an efficient treatment on high-performance computing architectures. […]
May, 16

Winograd Algorithm for AdderNet

Adder neural network (AdderNet) is a new kind of deep model that replaces the original massive multiplications in convolutions by additions while preserving the high performance. Since the hardware complexity of additions is much lower than that of multiplications, the overall energy consumption is thus reduced significantly. To further optimize the hardware overhead of using […]
May, 16

Raster Time Series: Learning and Processing

As the amount of remote sensing data is increasing at a high rate, due to great improvements in sensor technology, efficient processing capabilities are of utmost importance. Remote sensing data from satellites is crucial in many scientific domains, like biodiversity and climate research. Because weather and climate are of particular interest for almost all living […]
May, 16

PeriPy – A High Performance OpenCL Peridynamics Package

This paper presents a lightweight, open-source and high-performance python package for solving peridynamics problems in solid mechanics. The development of this solver is motivated by the need for fast analysis tools to achieve the large number of simulations required for `outer-loop’ applications, including sensitivity analysis, uncertainty quantification and optimisation. Our python software toolbox utilises the […]
May, 9

Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs

The Portable Computing Language (PoCL) is a vendor independent open-source OpenCL implementation that aims to support a variety of compute devices in a single platform. Evaluating PoCL versus the Intel OpenCL implementation reveals significant performance drawbacks of PoCL on Intel CPUs – which run 92 % of the TOP500 list. Using a selection of benchmarks, […]
May, 9

Sylkan: Towards a Vulkan Compute Target Platform for SYCL

SYCL is a modern high-level C++ programming interface which excels at expressing data parallelism for heterogeneous hardware platforms in a programmer-friendly way, and is standardized by the Khronos Group. The latest version of the standard, SYCL 2020, removes the previous dependence of the specification and its implementations on an underlying OpenCL target, opening the door […]

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