47 research outputs found
Parallel MATALAB Techniques
In this chapter, we show why parallel MATLAB is useful, provide a comparison
of the different parallel MATLAB choices, and describe a number of applications
in Signal and Image Processing: Audio Signal Processing, Synthetic Aperture
Radar (SAR) Processing and Superconducting Quantum Interference Filters
(SQIFs). Each of these applications have been parallelized using different
methods (Task parallel and Data parallel techniques). The applications
presented may be considered representative of type of problems faced by signal
and image processing researchers. This chapter will also strive to serve as a
guide to new signal and image processing parallel programmers, by suggesting a
parallelization strategy that can be employed when developing a general
parallel algorithm. The objective of this chapter is to help signal and image
processing algorithm developers understand the advantages of using parallel
MATLAB to tackle larger problems while staying within the powerful environment
of MATLAB
A Linear Algebra Approach to Fast DNA Mixture Analysis Using GPUs
Analysis of DNA samples is an important step in forensics, and the speed of
analysis can impact investigations. Comparison of DNA sequences is based on the
analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5
base pairs. Current forensics approaches use 20 STR loci for analysis. The use
of single nucleotide polymorphisms (SNPs) has utility for analysis of complex
DNA mixtures. The use of tens of thousands of SNPs loci for analysis poses
significant computational challenges because the forensic analysis scales by
the product of the loci count and number of DNA samples to be analyzed. In this
paper, we discuss the implementation of a DNA sequence comparison algorithm by
re-casting the algorithm in terms of linear algebra primitives. By developing
an overloaded matrix multiplication approach to DNA comparisons, we can
leverage advances in GPU hardware and algoithms for Dense Generalized
Matrix-Multiply (DGEMM) to speed up DNA sample comparisons. We show that it is
possible to compare 2048 unknown DNA samples with 20 million known samples in
under 6 seconds using a NVIDIA K80 GPU.Comment: Accepted for publication at the 2017 IEEE High Performance Extreme
Computing conferenc
D4M 3.0: Extended Database and Language Capabilities
The D4M tool was developed to address many of today's data needs. This tool
is used by hundreds of researchers to perform complex analytics on unstructured
data. Over the past few years, the D4M toolbox has evolved to support
connectivity with a variety of new database engines, including SciDB.
D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo
database. Finally, an implementation using the Julia programming language is
also now available. In this article, we describe some of our latest additions
to the D4M toolbox and our upcoming D4M 3.0 release. We show through
benchmarking and scaling results that we can achieve fast SciDB ingest using
the D4M-SciDB connector, that using Graphulo can enable graph algorithms on
scales that can be memory limited, and that the Julia implementation of D4M
achieves comparable performance or exceeds that of the existing MATLAB(R)
implementation.Comment: IEEE HPEC 201
Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning
This paper presents a solution to the challenge of mitigating carbon
emissions from large-scale high performance computing (HPC) systems and
datacenters that host machine learning (ML) inference services. ML inference is
critical to modern technology products, but it is also a significant
contributor to datacenter compute cycles and carbon emissions. We introduce
Clover, a carbon-friendly ML inference service runtime system that balances
performance, accuracy, and carbon emissions through mixed-quality models and
GPU resource partitioning. Our experimental results demonstrate that Clover is
effective in substantially reducing carbon emissions while maintaining high
accuracy and meeting service level agreement (SLA) targets. Therefore, it is a
promising solution toward achieving carbon neutrality in HPC systems and
datacenters
Large Scale Organization and Inference of an Imagery Dataset for Public Safety
Video applications and analytics are routinely projected as a stressing and
significant service of the Nationwide Public Safety Broadband Network. As part
of a NIST PSCR funded effort, the New Jersey Office of Homeland Security and
Preparedness and MIT Lincoln Laboratory have been developing a computer vision
dataset of operational and representative public safety scenarios. The scale
and scope of this dataset necessitates a hierarchical organization approach for
efficient compute and storage. We overview architectural considerations using
the Lincoln Laboratory Supercomputing Cluster as a test architecture. We then
describe how we intelligently organized the dataset across LLSC and evaluated
it with large scale imagery inference across terabytes of data.Comment: Accepted for publication IEEE HPEC 201
Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems
The rapid growth in demand for HPC systems has led to a rise in energy
consumption and carbon emissions, which requires urgent intervention. In this
work, we present a comprehensive framework for analyzing the carbon footprint
of high-performance computing (HPC) systems, considering the carbon footprint
during both the hardware production and system operational stages. Our work
employs HPC hardware component carbon footprint modeling, regional carbon
intensity analysis, and experimental characterization of the system life cycle
to highlight the importance of quantifying the carbon footprint of an HPC
system holistically