Early experiences in application level I/O tracing on blue gene systems

Seelam, Seetharami; Chung, I-Hsin; Hong, Ding-Yong; Wen, Hui-Fang; Yu, Hao

doi:10.1109/ipdps.2008.4536550

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…There are a few tools focusing on tracing data I/O events, like HPCT-IO [5], LANL-Trace [8], IOT [7], and ScalaIOTrace [9], but these tools do not provide enough comprehensive trace analysis or data access representation.…”

Section: Related Workmentioning

confidence: 99%

“…However, there is a serious lack of tools for analyzing parallel I/O performance in a comprehensive manner and for converting the analyzed data into information that optimization techniques can use. The existing I/O analysis tools [5] [6] [7] [8] [9] have limited scope of I/O characterization. Few of these tools [5] [8] collect a lot of trace information about I/O calls and leave it for programmers to understand.…”

Section: Introductionmentioning

confidence: 99%

“…The existing I/O analysis tools [5] [6] [7] [8] [9] have limited scope of I/O characterization. Few of these tools [5] [8] collect a lot of trace information about I/O calls and leave it for programmers to understand. These tools do not provide the much needed analysis step to gain a clear insight into I/O characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Boosting Application-Specific Parallel I/O Optimization Using IOSIG

Yin

Byna

Song

et al. 2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Boosting Application-Specific Parallel I/O Optimization Using IOSIG

Yin

Byna

Song

et al. 2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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“…LANL-Trace [9], for instance, relies on general-purpose compute-node kernels and dynamically linked libraries, which are not available on IBM Blue Gene systems using the lightweight CNK kernel. HPCT-I/O [10] and IOT [7] are two examples of I/O tracing toolkits developed specifically for leadership-class architectures, respectively IBM Blue Gene and Cray XT. However, the results published so far have all been performed at small scale, so one cannot yet say how these toolkits will function at HPC scales.…”

Section: A Data Collectionmentioning

confidence: 99%

A Visual Network Analysis Method for Large-Scale Parallel I/O Systems

Sigovan

Muelder

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

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Abstract-Parallel applications rely on I/O to load data, store end results, and protect partial results from being lost to system failure. Parallel I/O performance thus has a direct and significant impact on application performance. Because supercomputer I/O systems are large and complex, one cannot directly analyze their activity traces. While several visual or automated analysis tools for large-scale HPC log data exist, analysis research in the high-performance computing field is geared toward computation performance rather than I/O performance. Additionally, existing methods usually do not capture the network characteristics of HPC I/O systems. We present a visual analysis method for I/O trace data that takes into account the fact that HPC I/O systems can be represented as networks. We illustrate performance metrics in a way that facilitates the identification of abnormal behavior or performance problems. We demonstrate our approach on I/O traces collected from existing systems at different scales.

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“…A wide variety of tools are available for capturing and analyzing I/O access from individual parallel applications, including IPM, HPCT-IO, LANL-Trace, IOT, and mpiP [20], [21], [22], [23], [24]. Multiple I/O tracing mechanisms were surveyed by Konwinski et al [25].…”

Section: Related Workmentioning

confidence: 99%

Understanding and Improving Computational Science Storage Access through Continuous Characterization

et al. 2011

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Abstract-Computational science applications are driving a demand for increasingly powerful storage systems. While many techniques are available for capturing the I/O behavior of individual application trial runs and specific components of the storage system, continuous characterization of a production system remains a daunting challenge for systems with hundreds of thousands of compute cores and multiple petabytes of storage. As a result, these storage systems are often designed without a clear understanding of the diverse computational science workloads they will support.In this study, we outline a methodology for scalable, continuous, systemwide I/O characterization that combines storage device instrumentation, static file system analysis, and a new mechanism for capturing detailed application-level behavior. This methodology allows us to quantify systemwide trends such as the way application behavior changes with job size, the "burstiness" of the storage system, and the evolution of file system contents over time. The data also can be examined by application domain to determine the most prolific storage users and also investigate how their I/O strategies correlate with I/O performance. At the most detailed level, our characterization methodology can also be used to focus on individual applications and guide tuning efforts for those applications.We demonstrate the effectiveness of our methodology by performing a multilevel, two-month study of Intrepid, a 557-teraflop IBM Blue Gene/P system. During that time, we captured application-level I/O characterizations from 6,481 unique jobs spanning 38 science and engineering projects with up to 163,840 processes per job. We also captured patterns of I/O activity in over 8 petabytes of block device traffic and summarized the contents of file systems containing over 191 million files. We then used the results of our study to tune example applications, highlight trends that impact the design of future storage systems, and identify opportunities for improvement in I/O characterization methodology.

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Early experiences in application level I/O tracing on blue gene systems

Cited by 17 publications

References 10 publications

Boosting Application-Specific Parallel I/O Optimization Using IOSIG

Boosting Application-Specific Parallel I/O Optimization Using IOSIG

A Visual Network Analysis Method for Large-Scale Parallel I/O Systems

Understanding and Improving Computational Science Storage Access through Continuous Characterization

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