Patrick Bridges | University of New Mexico (original) (raw)

Papers by Patrick Bridges

Lecture Notes in Computer Science, 2012

IEEE International Conference on High Performance Computing, Data, and Analytics, Nov 13, 2016

Next-generation systems face a wide range of new potential sources of application interference, i... more Next-generation systems face a wide range of new potential sources of application interference, including resilience actions, system software adaptation, and in situ analytics programs. In this paper, we present a new model for analyzing the performance of bulk-synchronous HPC applications based on the use of extreme value theory. After validating this model against both synthetic and real applications, the paper then uses both simulation and modeling techniques to profile next-generation interference sources and characterize their behavior and performance impact on a selection of HPC benchmarks, mini-applications, and applications. Lastly, this work shows how the model can be used to understand how current interference mitigation techniques in multi-processors work.

Proceedings of the 23rd European MPI Users' Group Meeting

Scientific workloads running on current extreme-scale systems routinely generate tremendous volum... more Scientific workloads running on current extreme-scale systems routinely generate tremendous volumes of data for postprocessing. This data movement has become a serious issue due to its energy cost and the fact that I/O bandwidths have not kept pace with data generation rates. In situ analytics is an increasingly popular alternative in which post-simulation processing is embedded into an application, running as part of the same MPI job. This can reduce data movement costs but introduces a new potential source of interference for the application. Using a validated simulation-based approach, we investigate how best to mitigate the interference from time-shared in situ tasks for a number of key extreme-scale workloads. This paper makes a number of contributions. First, we show that the independent scheduling of in situ analytics tasks can significantly degradation application performance, with slowdowns exceeding 1000%. Second, we demonstrate that the degree of synchronization found in many modern collective algorithms is sufficient to significantly reduce the overheads of this interference to less than 10% in most cases. Finally, we show that many applications already frequently invoke collective operations that use these synchronizing MPI algorithms. Therefore, the syncronization introduced by these MPI collective algorithms can be leveraged to efficiently schedule analytics tasks with minimal changes to existing applications. This paper provides critical analysis and guidance for MPI users and developers on the importance of scheduling in situ analytics tasks. It shows the degree of synchronization needed to mitigate the performance impacts of these time-shared coupled codes and demonstrates how that synchronization can be realized in an extreme-scale environment using modern collective algorithms.

2017 IEEE International Conference on Cluster Computing (CLUSTER), 2017

Aggregating millions of hardware components to construct an exascale computing platform will pose... more Aggregating millions of hardware components to construct an exascale computing platform will pose significant resilience challenges. In addition to slowdowns associated with detected errors, silent errors are likely to further degrade application performance. Moreover, silent data corruption (SDC) has the potential to undermine the integrity of the results produced by important scientific applications.In this paper, we propose an application-independent mechanism to efficiently detect and correct SDC in read-mostly memory, where SDC may be most likely to occur. We use memory protection mechanisms to maintain compressed backups of application memory. We detect SDC by identifying changes in memory contents that occur without explicit write operations. We demonstrate that, for several applications, our approach can potentially protect a significant fraction of application memory pages from SDC with modest overheads. Moreover, our proposed technique can be straightforwardly combined with many other approaches to provide a significant bulwark against SDC.

SC16: International Conference for High Performance Computing, Networking, Storage and Analysis, 2016

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2016

Next-generation applications increasingly rely on in situ analytics to guide computation, reduce ... more Next-generation applications increasingly rely on in situ analytics to guide computation, reduce the amount of I/O performed, and perform other important tasks. Scheduling where and when to run analytics is challenging, however. This paper quantifies the costs and benefits of different approaches to scheduling applications and analytics on nodes in large-scale applications, including space sharing, uncoordinated time sharing, and gang scheduled time sharing.

This poster presents an HPC application workflow system whose goal is to provide verifiably-repro... more This poster presents an HPC application workflow system whose goal is to provide verifiably-reproducible HPC application performance. This system combines existing container, experiment, and data management techniques with HPC performance models, allowing it to both maximize performance reproducibility and inform users when application performance deviates from what should be expected even when running at scales or for lengths of time at which the application had never run.

Next-generation exascale systems, those capable of performing a quintillion (1018) operations per... more Next-generation exascale systems, those capable of performing a quintillion (1018) 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 checkpoints) 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.

International Journal of High Performance Computing Applications, Mar 18, 2012

2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), 2020

Performance variation deriving from hardware and software sources is common in modern scientific ... more Performance variation deriving from hardware and software sources is common in modern scientific and data-intensive computing systems, and synchronization in parallel and distributed programs often exacerbates their impacts at scale. The decentralized and emergent effects of such variation are, unfortunately, also difficult to systematically measure, analyze, and predict; modeling assumptions which are stringent enough to make analysis tractable frequently cannot be guaranteed at meaningful application scales, and longitudinal methods at such scales can require the capture and manipulation of impractically large amounts of data. This paper describes a new, scalable, and statistically robust approach for effective modeling, measurement, and analysis of large-scale performance variation in HPC systems. Our approach avoids the need to reason about complex distributions of runtimes among large numbers of individual application processes by focusing instead on the maximum length of distributed workload intervals. We describe this approach and its implementation in MPI which makes it applicable to a diverse set of HPC workloads. We also present evaluations of these techniques for quantifying and predicting performance variation carried out on large-scale computing systems, and discuss the strengths and limitations of the underlying modeling assumptions.

Proceedings of the 11th Workshop on Scientific Cloud Computing, 2020