Dataflow methods in HPC, visualisation and analysis

The processing power available to scientists and engineers using supercomputers over the last few decades has grown exponentially, permitting significantly more sophisticated simulations, and as a consequence, generating proportionally larger output datasets. This change has taken place in tandem with a gradual shift in the design and implementation of simulation and post-processing software, with a shift from simulation as a first step and visualisation/analysis as a second, towards in-situ on the fly methods that provide immediate visual feedback, place less strain on file-systems and reduce overall data-movement and copying. Concurrently, processor speed increases have dramatically slowed and multi and many-core architectures have instead become the norm for virtually all High Performance computing (HPC) machines. This in turn has led to a shift away from the traditional distributed one rank per node model, to one rank per process, using multiple processes per multicore node, and then back towards one rank per node again, using distributed and multi-threaded frameworks combined. This thesis consists of a series of publications that demonstrate how software design for analysis and visualisation has tracked these architectural changes and pushed the boundaries of HPC visualisation using dataflow techniques in distributed environments. The first publication shows how support for the time dimension in parallel pipelines can be implemented, demonstrating how information flow within an application can be leveraged to optimise performance and add features such as analysis of time-dependent flows and comparison of datasets at different timesteps. A method of integrating dataflow pipelines with in-situ visualisation is subsequently presented, using asynchronous coupling of user driven GUI controls and a live simulation running on a supercomputer. The loose coupling of analysis and simulation allows for reduced IO, immediate feedback and the ability to change simulation parameters on the fly. A significant drawback of parallel pipelines is the inefficiency caused by improper load-balancing, particularly during interactive analysis where the user may select between different features of interest, this problem is addressed in the fourth publication by integrating a high performance partitioning library into the visualization pipeline and extending the information flow up and down the pipeline to support it. This extension is demonstrated in the third publication (published earlier) on massive meshes with extremely high complexity and shows that general purpose visualization tools such as ParaView can be made to compete with bespoke software written for a dedicated task. The future of software running on many-core architectures will involve task-based runtimes, with dynamic load-balancing, asynchronous execution based on dataflow graphs, work stealing and concurrent data sharing between simulation and analysis. The final paper of this thesis presents an optimisation for one such runtime, in support of these future HPC applications

A Flexible Framework for Asynchronous In Situ and In Transit Analytics for Scientific Simulations

International audienceHigh performance computing systems are today composed of tens of thousands of processors and deep memory hierarchies. The next generation of machines will further increase the unbalance between I/O capabilities and processing power. To reduce the pressure on I/Os, the in situ analytics paradigm proposes to process the data as closely as possible to where and when the data are produced. Processing can be embedded in the simulation code, executed asynchronously on helper cores on the same nodes, or performed in transit on staging nodes dedicated to analytics. Today, software environ- nements as well as usage scenarios still need to be investigated before in situ analytics become a standard practice. In this paper we introduce a framework for designing, deploying and executing in situ scenarios. Based on a com- ponent model, the scientist designs analytics workflows by first developing processing components that are next assembled in a dataflow graph through a Python script. At runtime the graph is instantiated according to the execution context, the framework taking care of deploying the application on the target architecture and coordinating the analytics workflows with the simulation execution. Component coordination, zero- copy intra-node communications or inter-nodes data transfers rely on per-node distributed daemons. We evaluate various scenarios performing in situ and in transit analytics on large molecular dynamics systems sim- ulated with Gromacs using up to 1664 cores. We show in particular that analytics processing can be performed on the fraction of resources the simulation does not use well, resulting in a limited impact on the simulation performance (less than 6%). Our more advanced scenario combines in situ and in transit processing to compute a molecular surface based on the Quicksurf algorithm

A Fortran Kernel Generation Framework for Scientific Legacy Code

Quality assurance procedure is very important for software development. The complexity of modules and structure in software impedes the testing procedure and further development. For complex and poorly designed scientific software, module developers and software testers need to put a lot of extra efforts to monitor not related modules\u27 impacts and to test the whole system\u27s constraints. In addition, widely used benchmarks cannot help programmers with accurate and program specific system performance evaluation. In this situation, the generated kernels could provide considerable insight into better performance tuning. Therefore, in order to greatly improve the productivity of various scientific software engineering tasks such as performance tuning, debugging, and verification of simulation results, we developed an automatic compute kernel extraction prototype platform for complex legacy scientific code. In addition, considering that scientific research and experiment require long-term simulation procedure and the huge size of data transfer, we apply message passing based parallelization and I/O behavior optimization to highly improve the performance of the kernel extractor framework and then use profiling tools to give guidance for parallel distribution. Abnormal event detection is another important aspect for scientific research; dealing with huge observational datasets combined with simulation results it becomes not only essential but also extremely difficult. In this dissertation, for the sake of detecting high frequency event and low frequency events, we reconfigured this framework equipped with in-situ data transfer infrastructure. Through the method of combining signal processing data preprocess(decimation) with machine learning detection model to train the stream data, our framework can significantly decrease the amount of transferred data demand for concurrent data analysis (between distributed computing CPU/GPU nodes). Finally, the dissertation presents the implementation of the framework and a case study of the ACME Land Model (ALM) for demonstration. It turns out that the generated compute kernel with lower cost can be used in performance tuning experiments and quality assurance, which include debugging legacy code, verification of simulation results through single point and multiple points of variables tracking, collaborating with compiler vendors, and generating custom benchmark tests

Damaris: How to Efficiently Leverage Multicore Parallelism to Achieve Scalable, Jitter-free I/O

International audienceWith exascale computing on the horizon, the performance variability of I/O systems represents a key challenge in sustaining high performance. In many HPC applications, I/O is concurrently performed by all processes, which leads to I/O bursts. This causes resource contention and substantial variability of I/O performance, which significantly impacts the overall application performance and, most importantly, its predictability over time. In this paper, we propose a new approach to I/O, called Damaris, which leverages dedicated I/O cores on each multicore SMP node, along with the use of shared-memory, to efficiently perform asynchronous data processing and I/O in order to hide this variability. We evaluate our approach on three different platforms including the Kraken Cray XT5 supercomputer (ranked 11th in Top500), with the CM1 atmospheric model, one of the target HPC applications for the Blue Waters postpetascale supercomputer project. By overlapping I/O with computation and by gathering data into large files while avoiding synchronization between cores, our solution brings several benefits: 1) it fully hides jitter as well as all I/O-related costs, which makes simulation performance predictable; 2) it increases the sustained write throughput by a factor of 15 compared to standard approaches; 3) it allows almost perfect scalability of the simulation up to over 9,000 cores, as opposed to state-of-the-art approaches which fail to scale; 4) it enables a 600\% compression ratio without any additional overhead, leading to a major reduction of storage requirements

The Challenges of In Situ Analysis for Multiple Simulations

International audienceIn situ analysis and visualization have mainly been applied to the output of a single large-scale simulation. However, topics involving the execution of multiple simulations in supercomputers have only received minimal attention so far. Some important examples are uncertainty quantification, data assimilation, and complex optimization. In this position article, beyond highlighting the strengths and limitations of the tools that we have developed over the past few years, we share lessons learned from using them on large-scale platforms and from interacting with end users. We then discuss the forthcoming challenges, which future in situ analysis and vi-sualization frameworks will face when dealing with the exascale execution of multiple simulations

ExaViz: a Flexible Framework to Analyse, Steer and Interact with Molecular Dynamics Simulations

International audienceThe amount of data generated by molecular dynamics simulations of large molecular assemblies and the sheer size and complexity of the systems studied call for new ways to analyse, steer and interact with such calculations. Traditionally, the analysis is performed off-line once the huge amount of simulation results have been saved to disks, thereby stressing the supercomputer I/O systems, and making it increasingly difficult to handle post-processing and analysis from the scientist's office. The ExaViz framework is an alternative approach developed to couple the simulation with analysis tools to process the data as close as possible to their source of creation, saving a reduced, more manageable and pre-processed data set to disk. ExaViz supports a large variety of analysis and steering scenarios. Our framework can be used for live sessions (simulations short enough to be fully followed by the user) as well as batch sessions (long time batch executions). During interactive sessions, at run time, the user can display plots from analysis, visualise the molecular system and steer the simulation with a haptic device. We also emphasise how a Cave-like immersive environment could be used to leverage such simulations, offering a large display surface to view and intuitively navigate the molecular system

Performance Observability and Monitoring of High Performance Computing with Microservices

Traditionally, High Performance Computing (HPC) softwarehas been built and deployed as bulk-synchronous, parallel executables based on the message-passing interface (MPI) programming model. The rise of data-oriented computing paradigms and an explosion in the variety of applications that need to be supported on HPC platforms have forced a re-think of the appropriate programming and execution models to integrate this new functionality. In situ workflows demarcate a paradigm shift in HPC software development methodologies enabling a range of new applications --- from user-level data services to machine learning (ML) workflows that run alongside traditional scientific simulations. By tracing the evolution of HPC software developmentover the past 30 years, this dissertation identifies the key elements and trends responsible for the emergence of coupled, distributed, in situ workflows. This dissertation's focus is on coupled in situ workflows involving composable, high-performance microservices. After outlining the motivation to enable performance observability of these services and why existing HPC performance tools and techniques can not be applied in this context, this dissertation proposes a solution wherein a set of techniques gathers, analyzes, and orients performance data from different sources to generate observability. By leveraging microservice components initially designed to build high performance data services, this dissertation demonstrates their broader applicability for building and deploying performance monitoring and visualization as services within an in situ workflow. The results from this dissertation suggest that: (1) integration of performance data from different sources is vital to understanding the performance of service components, (2) the in situ (online) analysis of this performance data is needed to enable the adaptivity of distributed components and manage monitoring data volume, (3) statistical modeling combined with performance observations can help generate better service configurations, and (4) services are a promising architecture choice for deploying in situ performance monitoring and visualization functionality. This dissertation includes previously published and co-authored material and unpublished co-authored material

A characterization of workflow management systems for extreme-scale applications

Automation of the execution of computational tasks is at the heart of improving scientific productivity. Over the last years, scientific workflows have been established as an important abstraction that captures data processing and computation of large and complex scientific applications. By allowing scientists to model and express entire data processing steps and their dependencies, workflow management systems relieve scientists from the details of an application and manage its execution on a computational infrastructure. As the resource requirements of today’s computational and data science applications that process vast amounts of data keep increasing, there is a compelling case for a new generation of advances in high-performance computing, commonly termed as extreme-scale computing, which will bring forth multiple challenges for the design of workflow applications and management systems. This paper presents a novel characterization of workflow management systems using features commonly associated with extreme-scale computing applications. We classify 15 popular workflow management systems in terms of workflow execution models, heterogeneous computing environments, and data access methods. The paper also surveys workflow applications and identifies gaps for future research on the road to extreme-scale workflows and management systems