544 research outputs found
Design and optimization of a portable LQCD Monte Carlo code using OpenACC
The present panorama of HPC architectures is extremely heterogeneous, ranging
from traditional multi-core CPU processors, supporting a wide class of
applications but delivering moderate computing performance, to many-core GPUs,
exploiting aggressive data-parallelism and delivering higher performances for
streaming computing applications. In this scenario, code portability (and
performance portability) become necessary for easy maintainability of
applications; this is very relevant in scientific computing where code changes
are very frequent, making it tedious and prone to error to keep different code
versions aligned. In this work we present the design and optimization of a
state-of-the-art production-level LQCD Monte Carlo application, using the
directive-based OpenACC programming model. OpenACC abstracts parallel
programming to a descriptive level, relieving programmers from specifying how
codes should be mapped onto the target architecture. We describe the
implementation of a code fully written in OpenACC, and show that we are able to
target several different architectures, including state-of-the-art traditional
CPUs and GPUs, with the same code. We also measure performance, evaluating the
computing efficiency of our OpenACC code on several architectures, comparing
with GPU-specific implementations and showing that a good level of
performance-portability can be reached.Comment: 26 pages, 2 png figures, preprint of an article submitted for
consideration in International Journal of Modern Physics
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.Comment: 10 pages, 5 figures, 4 tables, More thorough discussion of
implementatio
- …