11,315 research outputs found
TensorFlow Doing HPC
TensorFlow is a popular emerging open-source programming framework supporting
the execution of distributed applications on heterogeneous hardware. While
TensorFlow has been initially designed for developing Machine Learning (ML)
applications, in fact TensorFlow aims at supporting the development of a much
broader range of application kinds that are outside the ML domain and can
possibly include HPC applications. However, very few experiments have been
conducted to evaluate TensorFlow performance when running HPC workloads on
supercomputers. This work addresses this lack by designing four traditional HPC
benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG)
solver and Fast Fourier Transform (FFT). We analyze their performance on two
supercomputers with accelerators and evaluate the potential of TensorFlow for
developing HPC applications. Our tests show that TensorFlow can fully take
advantage of high performance networks and accelerators on supercomputers.
Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical
communication bandwidth on our testing platform. We find an approximately 2x,
1.7x and 1.8x performance improvement when increasing the number of GPUs from
two to four in the matrix-matrix multiply, CG and FFT applications
respectively. All our performance results demonstrate that TensorFlow has high
potential of emerging also as HPC programming framework for heterogeneous
supercomputers.Comment: Accepted for publication at The Ninth International Workshop on
Accelerators and Hybrid Exascale Systems (AsHES'19
Overview of Parallel Platforms for Common High Performance Computing
The paper deals with various parallel platforms used for high performance computing in the signal processing domain. More precisely, the methods exploiting the multicores central processing units such as message passing interface and OpenMP are taken into account. The properties of the programming methods are experimentally proved in the application of a fast Fourier transform and a discrete cosine transform and they are compared with the possibilities of MATLAB's built-in functions and Texas Instruments digital signal processors with very long instruction word architectures. New FFT and DCT implementations were proposed and tested. The implementation phase was compared with CPU based computing methods and with possibilities of the Texas Instruments digital signal processing library on C6747 floating-point DSPs. The optimal combination of computing methods in the signal processing domain and new, fast routines' implementation is proposed as well
Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures
For reasons of both performance and energy efficiency, high-performance
computing (HPC) hardware is becoming increasingly heterogeneous. The OpenCL
framework supports portable programming across a wide range of computing
devices and is gaining influence in programming next-generation accelerators.
To characterize the performance of these devices across a range of applications
requires a diverse, portable and configurable benchmark suite, and OpenCL is an
attractive programming model for this purpose. We present an extended and
enhanced version of the OpenDwarfs OpenCL benchmark suite, with a strong focus
placed on the robustness of applications, curation of additional benchmarks
with an increased emphasis on correctness of results and choice of problem
size. Preliminary results and analysis are reported for eight benchmark codes
on a diverse set of architectures -- three Intel CPUs, five Nvidia GPUs, six
AMD GPUs and a Xeon Phi.Comment: 10 pages, 5 figure
Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance
The cubic Klein-Gordon equation is a simple but non-trivial partial
differential equation whose numerical solution has the main building blocks
required for the solution of many other partial differential equations. In this
study, the library 2DECOMP&FFT is used in a Fourier spectral scheme to solve
the Klein-Gordon equation and strong scaling of the code is examined on
thirteen different machines for a problem size of 512^3. The results are useful
in assessing likely performance of other parallel fast Fourier transform based
programs for solving partial differential equations. The problem is chosen to
be large enough to solve on a workstation, yet also of interest to solve
quickly on a supercomputer, in particular for parametric studies. Unlike other
high performance computing benchmarks, for this problem size, the time to
solution will not be improved by simply building a bigger supercomputer.Comment: 10 page
Improving Mobile SOC\u27s Performance as an Energy Efficient DSP Platform with Heterogeneous Computing
Mobile system-on-chip (SOC) technology is improving at a staggering rate spurred primarily by the adoption of smartphones and tablets. This rapid innovation has allowed the mobile SOC to be considered in everything from high performance computing to embedded applications. In this work, modern SOC\u27s heterogeneous computing capabilities are evaluated with a focus toward digital signal processing (DSP). Evaluation is conducted on modern consumer devices running Android operating system and leveraging the relatively new RenderScript Compute to utilize CPU resources alongside other compute resources such as graphics processing units (GPUs) and digital signal processors. In order to benchmark these concepts, several implementations of both the discrete Fourier transform (DFT) and the fast Fourier transform (FFT) are tested across devices. The results show both improvement in performance and energy efficiency on many devices compared to traditional Java implementations and indicate that the mobile SOC is a relevant platform for DSP applications
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