4,897 research outputs found
Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies
The nature of dark energy and the complete theory of gravity are two central
questions currently facing cosmology. A vital tool for addressing them is the
3-point correlation function (3PCF), which probes deviations from a spatially
random distribution of galaxies. However, the 3PCF's formidable computational
expense has prevented its application to astronomical surveys comprising
millions to billions of galaxies. We present Galactos, a high-performance
implementation of a novel, O(N^2) algorithm that uses a load-balanced k-d tree
and spherical harmonic expansions to compute the anisotropic 3PCF. Our
implementation is optimized for the Intel Xeon Phi architecture, exploiting
SIMD parallelism, instruction and thread concurrency, and significant L1 and L2
cache reuse, reaching 39% of peak performance on a single node. Galactos scales
to the full Cori system, achieving 9.8PF (peak) and 5.06PF (sustained) across
9636 nodes, making the 3PCF easily computable for all galaxies in the
observable universe.Comment: 11 pages, 7 figures, accepted to SuperComputing 201
Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability
Heterogeneous computing, which combines devices with different architectures,
is rising in popularity, and promises increased performance combined with
reduced energy consumption. OpenCL has been proposed as a standard for
programing such systems, and offers functional portability. It does, however,
suffer from poor performance portability, code tuned for one device must be
re-tuned to achieve good performance on another device. In this paper, we use
machine learning-based auto-tuning to address this problem. Benchmarks are run
on a random subset of the entire tuning parameter configuration space, and the
results are used to build an artificial neural network based model. The model
can then be used to find interesting parts of the parameter space for further
search. We evaluate our method with different benchmarks, on several devices,
including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970
GPU. Our model achieves a mean relative error as low as 6.1%, and is able to
find configurations as little as 1.3% worse than the global minimum.Comment: This is a pre-print version an article to be published in the
Proceedings of the 2015 IEEE International Parallel and Distributed
Processing Symposium Workshops (IPDPSW). For personal use onl
- …