9,216 research outputs found
A portable platform for accelerated PIC codes and its application to GPUs using OpenACC
We present a portable platform, called PIC_ENGINE, for accelerating
Particle-In-Cell (PIC) codes on heterogeneous many-core architectures such as
Graphic Processing Units (GPUs). The aim of this development is efficient
simulations on future exascale systems by allowing different parallelization
strategies depending on the application problem and the specific architecture.
To this end, this platform contains the basic steps of the PIC algorithm and
has been designed as a test bed for different algorithmic options and data
structures. Among the architectures that this engine can explore, particular
attention is given here to systems equipped with GPUs. The study demonstrates
that our portable PIC implementation based on the OpenACC programming model can
achieve performance closely matching theoretical predictions. Using the Cray
XC30 system, Piz Daint, at the Swiss National Supercomputing Centre (CSCS), we
show that PIC_ENGINE running on an NVIDIA Kepler K20X GPU can outperform the
one on an Intel Sandybridge 8-core CPU by a factor of 3.4
Parallel Performance of MPI Sorting Algorithms on Dual-Core Processor Windows-Based Systems
Message Passing Interface (MPI) is widely used to implement parallel
programs. Although Windowsbased architectures provide the facilities of
parallel execution and multi-threading, little attention has been focused on
using MPI on these platforms. In this paper we use the dual core Window-based
platform to study the effect of parallel processes number and also the number
of cores on the performance of three MPI parallel implementations for some
sorting algorithms
Even faster sorting of (not only) integers
In this paper we introduce RADULS2, the fastest parallel sorter based on
radix algorithm. It is optimized to process huge amounts of data making use of
modern multicore CPUs. The main novelties include: extremely optimized
algorithm for handling tiny arrays (up to about a hundred of records) that
could appear even billions times as subproblems to handle and improved
processing of larger subarrays with better use of non-temporal memory stores
An empirical evaluation of High-Level Synthesis languages and tools for database acceleration
High Level Synthesis (HLS) languages and tools are emerging as the most promising technique to make FPGAs more accessible to software developers. Nevertheless, picking the most suitable HLS for a certain class of algorithms depends on requirements such as area and throughput, as well as on programmer experience. In this paper, we explore the different trade-offs present when using a representative set of HLS tools in the context of Database Management Systems (DBMS) acceleration. More specifically, we conduct an empirical analysis of four representative frameworks (Bluespec SystemVerilog, Altera OpenCL, LegUp and Chisel) that we utilize to accelerate commonly-used database algorithms such as sorting, the median operator, and hash joins. Through our implementation experience and empirical results for database acceleration, we conclude that the selection of the most suitable HLS depends on a set of orthogonal characteristics, which we highlight for each HLS framework.Peer ReviewedPostprint (author’s final draft
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