Extracting Critical Path Graphs from MPI Applications

The critical path is one of the fundamental runtime characteristics of a parallel program. It identifies the longest execution sequence without wait delays. In other words, the critical path is the global execution path that inflicts wait operations on other nodes without itself being stalled. Hence, it dictates the overall runtime and knowing it is important to understand an application's runtime and message behavior and to target optimizations. We have developed a toolset that identifies the critical path of MPI applications, extracts it, and then produces a graphical representation of the corresponding program execution graph to visualize it. To implement this, we intercept all MPI library calls, use the information to build the relevant subset of the execution graph, and then extract the critical path from there. We have applied our technique to several scientific benchmarks and successfully produced critical path diagrams for applications running on up to 128 processors

A timed-automata approach for critical path detection in a soft real-time application

In this paper, we report preliminary ideas from our project called “Time Performance Improvement With Parallel Processing Systems” (TIPS). In the TIPS project, we plan to take advantage of multi-core platforms for performance improvement by parallelizing a complex soft real-time application. In order to increase the timing performance, one needs to adapt the optimizations on the critical execution paths of an application which are both significantly time consuming and important from user requirements' perspective. In this work, we present an approach how to detect critical paths in a target application

Overlapping communication and computation by using a hybrid MPI/SMPSs approach

A previous version of this document was submitted for publication by october 2008.Communication overhead is one of the dominant factors that affect performance in high-performance computing systems. To reduce the negative impact of communication, programmers overlap communication and computation by using asynchronous communication primitives. This increases code complexity, requiring more effort to write parallel code and making less readable code. This paper presents the hybrid use of MPI and SMPSs (SMP superscalar), a task-based shared-memory programming model, enhanced with a restart mechanism allowing the programmer to introduce the asynchronism that is necessary to enable the effective communication/computation overlap in a productive way. We demonstrate the hybrid use of MPI/SMPSs with the high-performance LINPACK benchmark, which uses the lookahead technique to overlap communication and computation. MPI/SMPSs improves the performance of a pure MPI with look-ahead by 7,6% on a 1024 processors machine. In addition to better performance, hybrid MPI/SMPSs substantially reduces code complexity, it is less sensitive to network bandwidth and operating system noise, and improves the use of main memory.Postprint (published version

Workflow Critical Path: A Data-oriented Critical path metric for Holistic HPC Workflows

Current trends in HPC, such as the push to exascale, convergence with Big Data, and growing complexity of HPC applications, have created gaps that traditional performance tools do not cover. One example is Holistic HPC Workflows — HPC workflows comprising multiple codes, paradigms, or platforms that are not developed using a workflow management system. To diagnose the performance of these applications, we define a new metric called Workflow Critical Path (WCP), a data-oriented metric for Holistic HPC Workflows. WCP constructs graphs that span across the workflow codes and platforms, using data states as vertices and data mutations as edges. Using cloud-based technologies, we implement a prototype called Crux, a distributed analysis tool for calculating and visualizing WCP. Our experiments with a workflow simulator on Amazon Web Services show Crux is scalable and capable of correctly calculating WCP for common Holistic HPC workflow patterns. We explore the use of WCP and discuss how Crux could be used in a production HPC environment

HDOT — An approach towards productive programming of hybrid applications

bulk synchronous parallel (BSP) communication model can hinder performance increases. This is due to the complexity to handle load imbalances, to reduce serialisation imposed by blocking communication patterns, to overlap communication with computation and, finally, to deal with increasing memory overheads. The MPI specification provides advanced features such as non-blocking calls or shared memory to mitigate some of these factors. However, applying these features efficiently usually requires significant changes on the application structure. Task parallel programming models are being developed as a means of mitigating the abovementioned issues but without requiring extensive changes on the application code. In this work, we present a methodology to develop hybrid applications based on tasks called hierarchical domain over-decomposition with tasking (HDOT). This methodology overcomes most of the issues found on MPI-only and traditional hybrid MPI+OpenMP applications. However, by emphasising the reuse of data partition schemes from process-level and applying them to task-level, it enables a natural coexistence between MPI and shared-memory programming models. The proposed methodology shows promising results in terms of programmability and performance measured on a set of applications.This work has been developed with the support of the European Union H2020 program through the INTERTWinE project (agreement number 671602); the Severo Ochoa Program awarded by the Spanish Government (SEV-2015-0493); the Generalitat de Catalunya (contract 2017-SGR-1414); and the Spanish Ministry of Science and Innovation (TIN2015-65316-P, Computaci on de Altas Prestaciones VII). The authors gratefully acknowledge Dr. Arnaud Mura, CNRS researcher at Institut PPRIME in France, for the numerical tool CREAMS. Finally, the manuscript has greatly bene ted from the precise comments of the reviewers.Peer ReviewedPostprint (author's final draft

Doctor of Philosophy

dissertationAlmost all high performance computing applications are written in MPI, which will continue to be the case for at least the next several years. Given the huge and growing importance of MPI, and the size and sophistication of MPI codes, scalable and incisive MPI debugging tools are essential. Existing MPI debugging tools have, despite their strengths, many glaring de ficiencies, especially when it comes to debugging under the presence of nondeterminism related bugs, which are bugs that do not always show up during testing. These bugs usually become manifest when the systems are ported to di fferent platforms for production runs. This dissertation focuses on the problem of developing scalable dynamic verifi cation tools for MPI programs that can provide a coverage guarantee over the space of MPI nondeterminism. That is, the tools should be able to detect diff erent outcomes of nondeterministic events in an MPI program and enforce all those di fferent outcomes through repeated executions of the program with the same test harness. We propose to achieve the coverage guarantee by introducing efficient distributed causality tracking protocols that are based on the matches-before order. The matches-before order is introduced to address the shortcomings of the Lamport happens-before order [40], which is not sufficient to capture causality for MPI program executions due to the complexity of the MPI semantics. The two protocols we propose are the Lazy Lamport Clocks Protocol (LLCP) and the Lazy Vector Clocks Protocol (LVCP). LLCP provides good scalability with a small possibility of missing potential outcomes of nondeterministic events while LVCP provides full coverage guarantee with a scalability tradeoff . In practice, we show through our experiments that LLCP provides the same coverage as LVCP. This thesis makes the following contributions: •The MPI matches-before order that captures the causality between MPI events in an MPI execution. • Two distributed causality tracking protocols for MPI programs that rely on the matches-before order. • A Distributed Analyzer for MPI programs (DAMPI), which implements the two aforementioned protocols to provide scalable and modular dynamic verifi cation for MPI programs. • Scalability enhancement through algorithmic improvements for ISP, a dynamic verifi er for MPI programs