Hello ADIOS: The Challenges and Lessons of Developing Leadership Class I/O Frameworks
Proposed for publication in Concurrency and Computation: Practice and Experience.
Abstract not provided.
Publications
In-Situ Visualization with Catalyst
Abstract not provided.
D2T: Doubly Distributed Transactions for High Performance and Distributed Computing
Abstract not provided.
D2T: Doubly Distributed Transactions for High Performance and Distributed Computing
Abstract not provided.
D2T: Doubly Distributed Transactions for High Performance and Distributed Computing
Abstract not provided.
Developing Integrated Data Services for Cray Systems with a Gemini Interconnect
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Developing Integrated Data Services for Cray Systems with a Gemini Interconnect
Abstract not provided.
Demonstration of a Legacy Application's Path to Exascale - ASC L2 Milestone 4467
Abstract not provided.
Report of experiments and evidence for ASC L2 milestone 4467 : demonstration of a legacy application's path to exascale
This report documents thirteen of Sandia's contributions to the Computational Systems and Software Environment (CSSE) within the Advanced Simulation and Computing (ASC) program between fiscal years 2009 and 2012. It describes their impact on ASC applications. Most contributions are implemented in lower software levels allowing for application improvement without source code changes. Improvements are identified in such areas as reduced run time, characterizing power usage, and Input/Output (I/O). Other experiments are more forward looking, demonstrating potential bottlenecks using mini-application versions of the legacy codes and simulating their network activity on Exascale-class hardware. The purpose of this report is to prove that the team has completed milestone 4467-Demonstration of a Legacy Application's Path to Exascale. Cielo is expected to be the last capability system on which existing ASC codes can run without significant modifications. This assertion will be tested to determine where the breaking point is for an existing highly scalable application. The goal is to stretch the performance boundaries of the application by applying recent CSSE RD in areas such as resilience, power, I/O, visualization services, SMARTMAP, lightweight LWKs, virtualization, simulation, and feedback loops. Dedicated system time reservations and/or CCC allocations will be used to quantify the impact of system-level changes to extend the life and performance of the ASC code base. Finally, a simulation of anticipated exascale-class hardware will be performed using SST to supplement the calculations. Determine where the breaking point is for an existing highly scalable application: Chapter 15 presented the CSSE work that sought to identify the breaking point in two ASC legacy applications-Charon and CTH. Their mini-app versions were also employed to complete the task. There is no single breaking point as more than one issue was found with the two codes. The results were that applications can expect to encounter performance issues related to the computing environment, system software, and algorithms. Careful profiling of runtime performance will be needed to identify the source of an issue, in strong combination with knowledge of system software and application source code.
Extreme-Scale Analytics: Controlling and Provisioning Online Analytics for Dynamic End User Requirements
Abstract not provided.
Valuing and Managing Data Based on Embodied Energy
Abstract not provided.
Supporting Integrated Data Services: A New Challenge for HPC Computing
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Extending scalability of collective IO through nessie and staging
PDSW'11 - Proceedings of the 6th Parallel Data Storage Workshop, Co-located with SC'11
The increasing fidelity of scientific simulations as they scale towards exascale sizes is straining the proven IO techniques championed throughout terascale computing. Chief among the successful IO techniques is the idea of collective IO where processes coordinate and exchange data prior to writing to storage in an effort to reduce the number of small, independent IO operations. As well as collective IO works for efficiently creating a data set in the canonical order, 3-D domain decompositions prove troublesome due to the amount of data exchanged prior to writing to storage. When each process has a tiny piece of a 3-D simulation space rather than a complete 'pencil' or 'plane', 2-D or 1-D domain decompositions respectively, the communication overhead to rearrange the data can dwarf the time spent actually writing to storage [27]. Our approach seeks to transparently increase scalability and performance while maintaining both the IO routines in the application and the final data format in the storage system. Accomplishing this leverages both the Nessie [23] RPC framework and a staging area with staging services. Through these tools, we employ a variety of data processing operations prior to invoking the native API to write data to storage yielding as much as a 3X performance improvement over the native calls. © 2011 ACM.
Extending Scalability of Collective IO Through Nessie and Staging
Abstract not provided.
Examples of In Transit Visualization (Presentation Slides)
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Resilient data staging through MxN distributed transactions
Scientific computing-driven discoveries are frequently driven from workflows that use persistent storage as a staging area for data between operations. With the bad and progressively worse bandwidth vs. data size issues as we continue towards exascale, eliminating persistent storage through techniques like data staging will both enable these workflows to continue online, but also enable more interactive workflows reducing the time to scientific discoveries. Data staging has shown to be an effective way for applications running on high-end computing platforms to offload expensive I/O operations and to manage the tremendous amounts of data they produce. This data staging approach, however, lacks the ACID style guarantees traditional straight-to-disk methods provide. Distributed transactions are a proven way to add ACID properties to data movements, however distributed transactions follow 1xN data movement semantics, where our highly parallel HPC environments employ MxN data movement semantics. In this paper we present a novel protocol that extends distributed transaction terminology to include MxN semantics which allows our data staging areas to benefit from ACID properties. We show that with our protocol we can provide resilient data staging with a limited performance penalty over current data staging implementations.
Data Services and Trilinos: Addressing I/O Challenges for Exascale Applications
Abstract not provided.
Data Services and Trilinos: A Brief Introduction to Trios Data Services
Abstract not provided.
Examples of In Transit Visualization
Abstract not provided.
Keeping checkpoint/restart viable for exascale systems
Next-generation exascale systems, those capable of performing a quintillion (10{sup 18}) operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoint) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms.
Addressing Scalable I/O Challenges for Exascale
Abstract not provided.
rMPI : increasing fault resiliency in a message-passing environment
As High-End Computing machines continue to grow in size, issues such as fault tolerance and reliability limit application scalability. Current techniques to ensure progress across faults, like checkpoint-restart, are unsuitable at these scale due to excessive overheads predicted to more than double an applications time to solution. Redundant computation, long used in distributed and mission critical systems, has been suggested as an alternative to checkpoint-restart on its own. In this paper we describe the rMPI library which enables portable and transparent redundant computation for MPI applications. We detail the design of the library as well as two replica consistency protocols, outline the overheads of this library at scale on a number of real-world applications, and finally outline the significant increase in an applications time to solution at extreme scale as well as show the scenarios in which redundant computation makes sense.
Evaluating the Viability of Process Replication Reliability for Exascale Systems
Abstract not provided.
Final report for %22High performance computing for advanced national electric power grid modeling and integration of solar generation resources%22, LDRD Project No. 149016
Design and operation of the electric power grid (EPG) relies heavily on computational models. High-fidelity, full-order models are used to study transient phenomena on only a small part of the network. Reduced-order dynamic and power flow models are used when analysis involving thousands of nodes are required due to the computational demands when simulating large numbers of nodes. The level of complexity of the future EPG will dramatically increase due to large-scale deployment of variable renewable generation, active load and distributed generation resources, adaptive protection and control systems, and price-responsive demand. High-fidelity modeling of this future grid will require significant advances in coupled, multi-scale tools and their use on high performance computing (HPC) platforms. This LDRD report demonstrates SNL's capability to apply HPC resources to these 3 tasks: (1) High-fidelity, large-scale modeling of power system dynamics; (2) Statistical assessment of grid security via Monte-Carlo simulations of cyber attacks; and (3) Development of models to predict variability of solar resources at locations where little or no ground-based measurements are available.
Six Degrees of Scientific Data: Reading Patterns for Extreme Scale Science IO
Abstract not provided.
Results 51–75 of 95