363,229 research outputs found
Lecture 11: The Road to Exascale and Legacy Software for Dense Linear Algebra
In this talk, we will look at the current state of high performance computing and look at the next stage of extreme computing. With extreme computing, there will be fundamental changes in the character of floating point arithmetic and data movement. In this talk, we will look at how extreme-scale computing has caused algorithm and software developers to change their way of thinking on implementing and program-specific applications
Enabling On-Demand Database Computing with MIT SuperCloud Database Management System
The MIT SuperCloud database management system allows for rapid creation and
flexible execution of a variety of the latest scientific databases, including
Apache Accumulo and SciDB. It is designed to permit these databases to run on a
High Performance Computing Cluster (HPCC) platform as seamlessly as any other
HPCC job. It ensures the seamless migration of the databases to the resources
assigned by the HPCC scheduler and centralized storage of the database files
when not running. It also permits snapshotting of databases to allow
researchers to experiment and push the limits of the technology without
concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC)
conference 2015. arXiv admin note: text overlap with arXiv:1406.492
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques
(AI) from the research community and industry to tackle "real world" problems,
requires High Performance Computing (HPC) resources to efficiently compute and
scale complex algorithms across thousands of nodes. Unfortunately, typical data
scientists are not familiar with the unique requirements and characteristics of
HPC environments. They usually develop their applications with high-level
scripting languages or frameworks such as TensorFlow and the installation
process often requires connection to external systems to download open source
software during the build. HPC environments, on the other hand, are often based
on closed source applications that incorporate parallel and distributed
computing API's such as MPI and OpenMP, while users have restricted
administrator privileges, and face security restrictions such as not allowing
access to external systems. In this paper we discuss the issues associated with
the deployment of AI frameworks in a secure HPC environment and how we
successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing
Conferenc
Training Behavior of Sparse Neural Network Topologies
Improvements in the performance of deep neural networks have often come
through the design of larger and more complex networks. As a result, fast
memory is a significant limiting factor in our ability to improve network
performance. One approach to overcoming this limit is the design of sparse
neural networks, which can be both very large and efficiently trained. In this
paper we experiment training on sparse neural network topologies. We test
pruning-based topologies, which are derived from an initially dense network
whose connections are pruned, as well as RadiX-Nets, a class of network
topologies with proven connectivity and sparsity properties. Results show that
sparse networks obtain accuracies comparable to dense networks, but extreme
levels of sparsity cause instability in training, which merits further study.Comment: 6 pages. Presented at the 2019 IEEE High Performance Extreme
Computing (HPEC) Conference. Received "Best Paper" awar
Benchmarking SciDB Data Import on HPC Systems
SciDB is a scalable, computational database management system that uses an
array model for data storage. The array data model of SciDB makes it ideally
suited for storing and managing large amounts of imaging data. SciDB is
designed to support advanced analytics in database, thus reducing the need for
extracting data for analysis. It is designed to be massively parallel and can
run on commodity hardware in a high performance computing (HPC) environment. In
this paper, we present the performance of SciDB using simulated image data. The
Dynamic Distributed Dimensional Data Model (D4M) software is used to implement
the benchmark on a cluster running the MIT SuperCloud software stack. A peak
performance of 2.2M database inserts per second was achieved on a single node
of this system. We also show that SciDB and the D4M toolbox provide more
efficient ways to access random sub-volumes of massive datasets compared to the
traditional approaches of reading volumetric data from individual files. This
work describes the D4M and SciDB tools we developed and presents the initial
performance results. This performance was achieved by using parallel inserts, a
in-database merging of arrays as well as supercomputing techniques, such as
distributed arrays and single-program-multiple-data programming.Comment: 5 pages, 4 figures, IEEE High Performance Extreme Computing (HPEC)
2016, best paper finalis
Tree Contraction, Connected Components, Minimum Spanning Trees: a GPU Path to Vertex Fitting
Standard parallel computing operations are considered in the context of algorithms for solving 3D graph problems which have applications, e.g., in vertex finding in HEP. Exploiting GPUs for tree-accumulation and graph algorithms is challenging: GPUs offer extreme computational power and high memory-access bandwidth, combined with a model of fine-grained parallelism perhaps not suiting the irregular distribution of linked representations of graph data structures. Achieving data-race free computations may demand serialization through atomic transactions, inevitably producing poor parallel performance. A Minimum Spanning Tree algorithm for GPUs is presented, its implementation discussed, and its efficiency evaluated on GPU and multicore architectures
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