30 research outputs found
Benefit of DDN's IME-FUSE for I/O intensive HPC applications
Many scientific applications are limited by I/O performance offered by parallel file systems on conventional storage systems. Flash- based burst buffers provide significant better performance than HDD backed storage, but at the expense of capacity. Burst buffers are consid- ered as the next step towards achieving wire-speed of interconnect and providing more predictable low latency I/O, which are the holy grail of storage. A critical evaluation of storage technology is mandatory as there is no long-term experience with performance behavior for particular applica- tions scenarios. The evaluation enables data centers choosing the right products and system architects the integration in HPC architectures. This paper investigates the native performance of DDN-IME, a flash- based burst buffer solution. Then, it takes a closer look at the IME-FUSE file systems, which uses IMEs as burst buffer and a Lustre file system as back-end. Finally, by utilizing a NetCDF benchmark, it estimates the performance benefit for climate applications
Survey of storage systems for high-performance computing
In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing
Dataclay: A distributed data store for effective inter-player data sharing
In the Big Data era, both the academic community and industry agree that a crucial point to obtain the maximum benefits from the explosive data growth is integrating information from different sources, and also combining methodologies to analyze and process it. For this reason, sharing data so that third parties can build new applications or services based on it is nowadays a trend. Although most data sharing initiatives are based on public data, the ability to reuse data generated by private companies is starting to gain importance as some of them (such as Google, Twitter, BBC or New York Times) are providing access to part of their data. However, current solutions for sharing data with third parties are not fully convenient to either or both data owners and data consumers. Therefore we present dataClay, a distributed data store designed to share data with external players in a secure and flexible way based on the concepts of identity and encapsulation. We also prove that dataClay is comparable in terms of performance with trendy NoSQL technologies while providing extra functionality, and resolves impedance mismatch issues based on the Object Oriented paradigm for data representation.This work has been supported by the Spanish Government (grant SEV2015-0493 of the Severo Ochoa Program), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316) and by Generalitat de Catalunya (contract 2014-SGR-1051). Special thanks go to Dr. Oscar Romero (Universitat Politècnica de Catalunya) for providing helpful feedback on the paper.Peer ReviewedPostprint (published version
Flexible allocation and space management in storage systems
In this dissertation, we examine some of the challenges faced by the emerging
networked storage systems. We focus on two main issues. Current file systems allocate
storage statically at the time of their creation. This results in many suboptimal
scenarios, for example: (a) space on the disk is not allocated well across multiple
file systems, (b) data is not organized well for typical access patterns. We propose
Virtual Allocation for flexible storage allocation. Virtual allocation separates storage
allocation from the file system. It employs an allocate-on-write strategy, which lets
applications fit into the actual usage of storage space without regard to the configured
file system size. This improves flexibility by allowing storage space to be shared across
different file systems. We present the design of virtual allocation and an evaluation
of it through benchmarks based on a prototype system on Linux.
Next, based on virtual allocation, we consider the problem of balancing locality and load in networked storage systems with multiple storage devices (or bricks).
Data distribution affects locality and load balance across the devices in a networked
storage system. We propose user-optimal data migration scheme which tries to balance locality and load balance in such networked storage systems. The presented
approach automatically and transparently manages migration of data blocks among
disks as data access patterns and loads change over time. We built a prototype system on Linux and present the design of user-optimal migration and an evaluation of
it through realistic experiments
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Training deep neural networks on large scientific data is a challenging task
that requires enormous compute power, especially if no pre-trained models exist
to initialize the process. We present a novel tournament method to train
traditional as well as generative adversarial networks built on LBANN, a
scalable deep learning framework optimized for HPC systems. LBANN combines
multiple levels of parallelism and exploits some of the worlds largest
supercomputers. We demonstrate our framework by creating a complex predictive
model based on multi-variate data from high-energy-density physics containing
hundreds of millions of images and hundreds of millions of scalar values
derived from tens of millions of simulations of inertial confinement fusion.
Our approach combines an HPC workflow and extends LBANN with optimized data
ingestion and the new tournament-style training algorithm to produce a scalable
neural network architecture using a CORAL-class supercomputer. Experimental
results show that 64 trainers (1024 GPUs) achieve a speedup of 70.2 over a
single trainer (16 GPUs) baseline, and an effective 109% parallel efficiency