Ciprian Docan

DataSpaces: an interaction and coordination framework for coupled simulation workflows

Emerging high-performance distributed computing environments are enabling new end-to-end formulations in science and engineering that involve multiple interacting processes and data-intensive application workflows. For example, current fusion simulation efforts are exploring coupled models and codes that simultaneously simulate separate application processes, such as the core and the edge turbulence. These components run on different high performance computing resources, need to interact at runtime with each other and with services for data monitoring, data analysis and visualization, and data archiving. As a result, they require efficient and scalable support for dynamic and flexible couplings and interactions, which remains a challenge. This paper presents DataSpaces a flexible interaction and coordination substrate that addresses this challenge. DataSpaces essentially implements a semantically specialized virtual shared space abstraction that can be associatively accessed by all components and services in the application workflow. It enables live data to be extracted from running simulation components, indexes this data online, and then allows it to be monitored, queried and accessed by other components and services via the space using semantically meaningful operators. The underlying data transport is asynchronous, low-overhead and largely memory-to-memory. The design, implementation, and experimental evaluation of DataSpaces using a coupled fusion simulation workflow is presented.

Low-cost Virtual Rehabilitation of the Hand for Patients Post-Stroke

We are witnessing the convergence of game technology (both software and hardware) with rehabilitation science to form a second-generation virtual rehabilitation framework. This is fortunate in view of the need to reduce system costs and thus facilitate adoption in clinical practice. This paper presents an Xbox-based physical rehabilitation system currently under development at Rutgers University. Unlike its high-end precursor aimed at hand training for patients post-stroke, the experimental system described here uses an inexpensive P5 game glove and Java 3D simulations. This results in significant cost savings, albeit with some tradeoff in functionality

Enabling In-situ Execution of Coupled Scientific Workflow on Multi-core Platform

Emerging scientific application workflows are composed of heterogeneous coupled component applications that simulate different aspects of the physical phenomena being modeled, and that interact and exchange significant volumes of data at runtime. With the increasing performance gap between on-chip data sharing and off-chip data transfers in current systems based on multicore processors, moving large volumes of data using communication network fabric can significantly impact performance. As a result, minimizing the amount of inter-application data exchanges that are across compute nodes and use the network is critical to achieving overall application performance and system efficiency. In this paper, we investigate the in-situ execution of the coupled components of a scientific application workflow so as to maximize on-chip exchange of data. Specifically, we present a distributed data sharing and task execution framework that (1) employs data-centric task placement to map computations from the coupled applications onto processor cores so that a large portion of the data exchanges can be performed using the intra-node shared memory, (2) provides a shared space programming abstraction that supplements existing parallel programming models (e.g., message passing) with specialized one-sided asynchronous data access operators and can be used to express coordination and data exchanges between the coupled components. We also present the implementation of the framework and its experimental evaluation on the Jaguar Cray XT5 at Oak Ridge National Laboratory.

PlayStation 3-based tele-rehabilitation for children with hemiplegia

The convergence of game technology (software and hardware), the Internet, and rehabilitation science forms the second-generation virtual rehabilitation framework. This reduced-cost and patient/therapist familiarity facilitate adoption in clinical practice. This paper presents a PlayStation 3-based hand physical rehabilitation system for children with hemiplegia due to perinatal brain injury (hemiplegic cerebral palsy) or later childhood stroke. Unlike precursor systems aimed at providing hand training for post-stroke adults in a clinical setting, the experimental system described here was developed for in-home tele-rehabilitation on a game console for children and adults with chronic hemiplegia after stroke or other focal brain injury. Significant improvements in Activities of Daily Living function followed three months of training at home on the system. Clinical trials are ongoing at this time.

Examples of in transit visualization

One of the most pressing issues with petascale analysis is the transport of simulation results data to a meaningful analysis. Traditional workflow prescribes storing the simulation results to disk and later retrieving them for analysis and visualization. However, at petascale this storage of the full results is prohibitive. A solution to this problem is to run the analysis and visualization concurrently with the simulation and bypass the storage of the full results. One mechanism for doing so is in transit visualization in which analysis and visualization is run on I/O nodes that receive the full simulation results but write information from analysis or provide run-time visualization. This paper describes the work in progress for three in transit visualization solutions, each using a different transport mechanism.

Parallel in situ indexing for data-intensive computing

As computing power increases exponentially, vast amount of data is created by many scientific research activities. However, the bandwidth for storing the data to disks and reading the data from disks has been improving at a much slower pace. These two trends produce an ever-widening data access gap. Our work brings together two distinct technologies to address this data access issue: indexing and in situ processing. From decades of database research literature, we know that indexing is an effective way to address the data access issue, particularly for accessing relatively small fraction of data records. As data sets increase in sizes, more and more analysts need to use selective data access, which makes indexing an even more important for improving data access. The challenge is that most implementations of indexing technology are embedded in large database management systems (DBMS), but most scientific datasets are not managed by any DBMS. In this work, we choose to include indexes with the scientific data instead of requiring the data to be loaded into a DBMS.We use compressed bitmap indexes from the FastBit software which are known to be highly effective for query-intensive workloads common to scientific data analysis. To use the indexes, we need to build them first. The index building procedure needs to access the whole data set and may also require a significant amount of compute time. In this work, we adapt the in situ processing technology to generate the indexes, thus removing the need of reading data from disks and to build indexes in parallel. The in situ data processing system used is ADIOS, a middleware for high-performance I/O. Our experimental results show that the indexes can improve the data access time up to 200 times depending on the fraction of data selected, and using in situ data processing system can effectively reduce the time needed to create the indexes, up to 10 times with our in situ technique when using identical parallel settings.

Moving the Code to the Data - Dynamic Code Deployment Using ActiveSpaces

Managing the large volumes of data produced by emerging scientific and engineering simulations running on leadership-class resources has become a critical challenge. The data has to be extracted off the computing nodes and transported to consumer nodes so that it can be processed, analyzed, visualized, archived, etc. Several recent research efforts have addressed data-related challenges at different levels. One attractive approach is to offload expensive I/O operations to a smaller set of dedicated computing nodes known as a staging area. However, even using this approach, the data still has to be moved from the staging area to consumer nodes for processing, which continues to be a bottleneck. In this paper, we investigate an alternate approach, namely moving the data-processing code to the staging area rather than moving the data. Specifically, we present the Active Spaces framework, which provides (1) programming support for defining the data-processing routines to be downloaded to the staging area, and (2) run-time mechanisms for transporting binary codes associated with these routines to the staging area, executing the routines on the nodes of the staging area, and returning the results. We also present an experimental performance evaluation of Active Spaces using applications running on the Cray XT5 at Oak Ridge National Laboratory. Finally, we use a coupled fusion application workflow to explore the trade-offs between transporting data and transporting the code required for data processing during coupling, and we characterize the sweet spots for each option.

DART: a substrate for high speed asynchronous data IO

Large scale simulations of complex physics phenomena have long run times and generate massive amounts of data. Saving this data to external storage systems or transferring it to remote locations for analysis is a costly operation that quickly becomes a performance bottleneck. In this paper, we present DART (Decoupled and Asynchronous Remote Transfers), an efficient data transfer substrate that effectively minimizes the data I/O overhead on the running simulations. DART is a thin software layer built on RDMA technology to enable fast, low-overhead and asynchronous access to data from a running simulation, and support high-throughput, low-latency data transfers.

EFFIS: An End-to-end Framework for Fusion Integrated Simulation

The purpose of the Fusion Simulation Project is to develop a predictive capability for integrated modeling of magnetically confined burning plasmas. In support of this mission, the Center for Plasma Edge Simulation has developed an End-to-end Framework for Fusion Integrated Simulation (EFFIS) that combines critical computer science technologies in an effective manner to support leadership class computing and the coupling of complex plasma physics models. We describe here the main components of EFFIS and how they are being utilized to address our goal of integrated predictive plasma edge simulation.

Experiments with Memory-to-Memory Coupling for End-to-End Fusion Simulation Workflows

Scientific applications are striving to accurately simulate multiple interacting physical processes that comprise complex phenomena being modeled. Efficient and scalable parallel implementations of these coupled simulations present challenging interaction and coordination requirements, especially when the coupled physical processes are computationally heterogeneous and progress at different speeds. In this paper, we present the design, implementation and evaluation of a memory-to-memory coupling framework for coupled scientific simulations on high-performance parallel computing platforms. The framework is driven by the coupling requirements of the Center for Plasma Edge Simulation, and it provides simple coupling abstractions as well as efficient asynchronous (RDMA-based) memory-to-memory data transport mechanisms that complement existing parallel programming systems and data sharing frameworks. The framework enables flexible coupling behaviors that are asynchronous in time and space, and it supports dynamic coupling between heterogeneous simulation processes without enforcing any synchronization constraints. We evaluate the performance and scalability of the coupling framework using a specific coupling scenario, on the Jaguar Cray XT5 system at Oak Ridge National Laboratory.