Learning from the Success of MPI

The Message Passing Interface (MPI) has been extremely successful as a portable way to program high-performance parallel computers. This success has occurred in spite of the view of many that message passing is difficult and that other approaches, including automatic parallelization and directive-based parallelism, are easier to use. This paper argues that MPI has succeeded because it addresses all of the important issues in providing a parallel programming model.Comment: 12 pages, 1 figur

Advanced semantics for accelerated graph processing

Large-scale graph applications are of great national, commercial, and societal importance, with direct use in fields such as counter-intelligence, proteomics, and data mining. Unfortunately, graph-based problems exhibit certain basic characteristics that make them a poor match for conventional computing systems in terms of structure, scale, and semantics. Graph processing kernels emphasize sparse data structures and computations with irregular memory access patterns that destroy the temporal and spatial locality upon which modern processors rely for performance. Furthermore, applications in this area utilize large data sets, and have been shown to be more data intensive than typical floating-point applications, two properties that lead to inefficient utilization of the hierarchical memory system. Current approaches to processing large graph data sets leverage traditional HPC systems and programming models, for shared memory and message-passing computation, and are thus limited in efficiency, scalability, and programmability. The research presented in this thesis investigates the potential of a new model of execution that is hypothesized as a promising alternative for graph-based applications to conventional practices. A new approach to graph processing is developed and presented in this thesis. The application of the experimental ParalleX execution model to graph processing balances continuation-migration style fine-grain concurrency with constraint-based synchronization through embedded futures. A collection of parallel graph application kernels provide experiment control drivers for analysis and evaluation of this innovative strategy. Finally, an experimental software library for scalable graph processing, the ParalleX Graph Library, is defined using the HPX runtime system, providing an implementation of the key concepts and a framework for development of ParalleX-based graph applications

Overlapping of Communication and Computation and Early Binding: Fundamental Mechanisms for Improving Parallel Performance on Clusters of Workstations

This study considers software techniques for improving performance on clusters of workstations and approaches for designing message-passing middleware that facilitate scalable, parallel processing. Early binding and overlapping of communication and computation are identified as fundamental approaches for improving parallel performance and scalability on clusters. Currently, cluster computers using the Message-Passing Interface for interprocess communication are the predominant choice for building high-performance computing facilities, which makes the findings of this work relevant to a wide audience from the areas of high-performance computing and parallel processing. The performance-enhancing techniques studied in this work are presently underutilized in practice because of the lack of adequate support by existing message-passing libraries and are also rarely considered by parallel algorithm designers. Furthermore, commonly accepted methods for performance analysis and evaluation of parallel systems omit these techniques and focus primarily on more obvious communication characteristics such as latency and bandwidth. This study provides a theoretical framework for describing early binding and overlapping of communication and computation in models for parallel programming. This framework defines four new performance metrics that facilitate new approaches for performance analysis of parallel systems and algorithms. This dissertation provides experimental data that validate the correctness and accuracy of the performance analysis based on the new framework. The theoretical results of this performance analysis can be used by designers of parallel system and application software for assessing the quality of their implementations and for predicting the effective performance benefits of early binding and overlapping. This work presents MPI/Pro, a new MPI implementation that is specifically optimized for clusters of workstations interconnected with high-speed networks. This MPI implementation emphasizes features such as persistent communication, asynchronous processing, low processor overhead, and independent message progress. These features are identified as critical for delivering maximum performance to applications. The experimental section of this dissertation demonstrates the capability of MPI/Pro to facilitate software techniques that result in significant application performance improvements. Specific demonstrations with Virtual Interface Architecture and TCP/IP over Ethernet are offered

Enabling collaboration in virtual reality navigators

In this paper we characterize a feature superset for Collaborative Virtual Reality Environments (CVRE), and derive a component framework to transform stand-alone VR navigators into full-fledged multithreaded collaborative environments. The contributions of our approach rely on a cost-effective and extensible technique for loading software components into separate POSIX threads for rendering, user interaction and network communications, and adding a top layer for managing session collaboration. The framework recasts a VR navigator under a distributed peer-to-peer topology for scene and object sharing, using callback hooks for broadcasting remote events and multicamera perspective sharing with avatar interaction. We validate the framework by applying it to our own ALICE VR Navigator. Experimental results show that our approach has good performance in the collaborative inspection of complex models.Postprint (published version

Doctor of Philosophy

dissertationHigh-performance supercomputers on the Top500 list are commonly designed around commodity CPUs. Most of the codes executed on these machines are message-passing codes using the message-passing toolkit (MPI). Thus it makes sense to look at these machines from a holistic systems architecture perspective and consider optimizations to commodity processors that make them more efficient in message-passing architectures. Described herein is a new User-Level Notification (ULN) architecture that significantly improves message-passing performance. The architecture integrates a simultaneous multithreaded (SMT) processor with a user-level network interface (NI) that can directly control the execution scheduling of threads on the processor. By allowing the network interface to control the execution of message handling code at the user level, the operating system (OS) related overhead for handling interrupts and user code dispatch related to notifications is eliminated. By using an SMT processor, message handling can be performed in one thread concurrent to user computation in other threads, thus most of the overhead of executing message handlers can be hidden. This dissertation presents measurements showing the OS overheads related to message-passing are significant in modern architectures and describes a new architecture that significantly reduces these overheads. On a communication-intensive real-world application, the ULN architecture provides a 50.9% performance improvement over a more traditional OS-based NIC and a 5.29-31.9% improvement over a best-of-class user-level NIC due to the user-level notifications

Systematic composition of distributed objects: Processes and sessions

We consider a system with the infrastructure for the creation and interconnection of large numbers of distributed persistent objects. This system is exemplified by the Internet: potentially, every appliance and document on the Internet has both persistent state and the ability to interact with large numbers of other appliances and documents on the Internet. This paper elucidates the characteristics of such a system, and proposes the compositional requirements of its corresponding infrastructure. We explore the problems of specifying, composing, reasoning about and implementing applications in such a system. A specific concern of our research is developing the infrastructure to support structuring distributed applications by using sequential, choice and parallel composition, in the anarchic environment where application compositions may be unforeseeable and interactions may be unknown prior to actually occurring. The structuring concepts discussed are relevant to a wide range of distributed applications; our implementation is illustrated with collaborative Java processes interacting over the Internet, but the methodology provided can be applied independent of specific platforms

Analysis of Performance-impacting Factors on Checkpointing Frameworks: The CPPC Case Study

This is a post-peer-review, pre-copyedit version of an article published in The Computer Journal. The final authenticated version is available online at: https://doi.org/10.1093/comjnl/bxr018[Abstract] This paper focuses on the performance evaluation of Compiler for Portable Checkpointing (CPPC), a tool for the checkpointing of parallel message-passing applications. Its performance and the factors that impact it are transparently and rigorously identified and assessed. The tests were performed on a public supercomputing infrastructure, using a large number of very different applications and showing excellent results in terms of performance and effort required for integration into user codes. Statistical analysis techniques have been used to better approximate the performance of the tool. Quantitative and qualitative comparisons with other rollback-recovery approaches to fault tolerance are also included. All these data and comparisons are then discussed in an effort to extract meaningful conclusions about the state-of-the-art and future research trends in the rollback-recovery field.Minsiterio de Ciencia e Innovación; TIN2010-1673

Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs

Hybrid parallel programming models that combine message passing (MP) and shared- memory multithreading (MT) are becoming more popular, especially with applications requiring higher degrees of parallelism and scalability. Consequently, coupled parallel programs, those built via the integration of independently developed and optimized software libraries linked into a single application, increasingly comprise message-passing libraries with differing preferred degrees of threading, resulting in thread-level heterogeneity. Retroactively matching threading levels between independently developed and maintained libraries is difficult, and the challenge is exacerbated because contemporary middleware services provide only static scheduling policies over entire program executions, necessitating suboptimal, over-subscribed or under-subscribed, configurations. In coupled applications, a poorly configured component can lead to overall poor application performance, suboptimal resource utilization, and increased time-to-solution. So it is critical that each library executes in a manner consistent with its design and tuning for a particular system architecture and workload. Therefore, there is a need for techniques that address dynamic, conflicting configurations in coupled multithreaded message-passing (MT-MP) programs. Our thesis is that we can achieve significant performance improvements over static under-subscribed approaches through reconfigurable execution environments that consider compute phase parallelization strategies along with both hardware and software characteristics. In this work, we present new ways to structure, execute, and analyze coupled MT- MP programs. Our study begins with an examination of contemporary approaches used to accommodate thread-level heterogeneity in coupled MT-MP programs. Here we identify potential inefficiencies in how these programs are structured and executed in the high-performance computing domain. We then present and evaluate a novel approach for accommodating thread-level heterogeneity. Our approach enables full utilization of all available compute resources throughout an application’s execution by providing programmable facilities with modest overheads to dynamically reconfigure runtime environments for compute phases with differing threading factors and affinities. Our performance results show that for a majority of the tested scientific workloads our approach and corresponding open-source reference implementation render speedups greater than 50 % over the static under-subscribed baseline. Motivated by our examination of reconfigurable execution environments and their memory overhead, we also study the memory attribution problem: the inability to predict or evaluate during runtime where the available memory is used across the software stack comprising the application, reusable software libraries, and supporting runtime infrastructure. Specifically, dynamic adaptation requires runtime intervention, which by its nature introduces additional runtime and memory overhead. To better understand the latter, we propose and evaluate a new way to quantify component-level memory usage from unmodified binaries dynamically linked to a message-passing communication library. Our experimental results show that our approach and corresponding implementation accurately measure memory resource usage as a function of time, scale, communication workload, and software or hardware system architecture, clearly distinguishing between application and communication library usage at a per-process level