Researchers from national laboratories and universities will be demonstrating new tools and technologies for accelerating data transfer, improving application performance and increasing energy efficiency in a series of demos scheduled across three days in the DOE booth at SC18.
Tuesday, Nov. 13
Demo Station 1
10 a.m.
“Real-time Performance Analysis of Applications and Workflow” – Gyorgy Matyasfalvi, Brookhaven National Laboratory
As part of the ECP CODAR project, Brookhaven National Laboratory, in collaboration with the Oregon Universities TAU team, has developed unique capabilities to analyze, reduce and visualize single application and complete workflow performance data in-situ. The resulting tool enables the researchers to examine and explore their workflow performance as it is being executed.
As part of the ECP CODAR project, Brookhaven National Laboratory, in collaboration with the Oregon Universities TAU team, has developed unique capabilities to analyze, reduce and visualize single application and complete workflow performance data in-situ. The resulting tool enables the researchers to examine and explore their workflow performance as it is being executed.
11 a.m.
“BigData Express: Toward Predictable, Schedulable, and High-performance Data Transfer” – Wenji Wu, Fermilab
In DOE research communities, the emergence of distributed, extreme-scale science applications is generating significant challenges regarding data transfer. The data transfer challenges of the extreme-scale era are typically characterized by two relevant dimensions: high-performance challenges and time-constraint challenges. To meet these challenges, DOE’s ASCR office has funded Fermilab and Oak Ridge National Laboratory to collaboratively work on the BigData Express project (http://bigdataexpress.fnal.gov). BigData Express seeks to provide a schedulable, predictable, and high-performance data transfer service for DOE’s large-scale science computing facilities and their collaborators. Software defined technologies are key enablers for BigData Express. In particular, BigData Express makes use of software-defined networking (SDN) and software-defined storage (SDS) to develop a data-transfer-centric architecture to optimally orchestrate the various resources in an end-to-end data transfer loop. With end-to-end integration and coordination, network congestion and storage IO contentions are effectively reduced or eliminated. As a result, data transfer performance is significantly improved. BigData Express has recently gained growing attention in the communities. The BigData Express software is being deployed at multiple research institutions, which include UMD, StarLight, FNAL, KISTI (South Korea), UVA, and Ciena. Meanwhile, the BigData Express research team is collaborating with StarLight to deploy BigData Express at various research platforms, including Pacific Research Platform, National Research Platform, and Global Research Platform. It is envisioned that we are working toward building a multi-domain and multi-tenant software defined infrastructure (SDI) for high-performance data transfer.In this demo, we use BDE software to demonstrate bulk data movement over wide area networks. Our goal is to demonstrate that BDE can successfully address the high-performance and time-constraint challenges of data transfer to support extreme-scale science applications.
In DOE research communities, the emergence of distributed, extreme-scale science applications is generating significant challenges regarding data transfer. The data transfer challenges of the extreme-scale era are typically characterized by two relevant dimensions: high-performance challenges and time-constraint challenges. To meet these challenges, DOE’s ASCR office has funded Fermilab and Oak Ridge National Laboratory to collaboratively work on the BigData Express project (http://bigdataexpress.fnal.gov). BigData Express seeks to provide a schedulable, predictable, and high-performance data transfer service for DOE’s large-scale science computing facilities and their collaborators. Software defined technologies are key enablers for BigData Express. In particular, BigData Express makes use of software-defined networking (SDN) and software-defined storage (SDS) to develop a data-transfer-centric architecture to optimally orchestrate the various resources in an end-to-end data transfer loop. With end-to-end integration and coordination, network congestion and storage IO contentions are effectively reduced or eliminated. As a result, data transfer performance is significantly improved. BigData Express has recently gained growing attention in the communities. The BigData Express software is being deployed at multiple research institutions, which include UMD, StarLight, FNAL, KISTI (South Korea), UVA, and Ciena. Meanwhile, the BigData Express research team is collaborating with StarLight to deploy BigData Express at various research platforms, including Pacific Research Platform, National Research Platform, and Global Research Platform. It is envisioned that we are working toward building a multi-domain and multi-tenant software defined infrastructure (SDI) for high-performance data transfer.In this demo, we use BDE software to demonstrate bulk data movement over wide area networks. Our goal is to demonstrate that BDE can successfully address the high-performance and time-constraint challenges of data transfer to support extreme-scale science applications.
12 p.m.
“ParaView Running on a Cluster” – W. Alan Scott, Sandia National Laboratories
ParaView will be running with large data on a remote cluster at Sandia National Laboratories.
ParaView will be running with large data on a remote cluster at Sandia National Laboratories.
1 p.m.
“Performance Evaluation using TAU and TAU Commander” – Sameer Shende, Sandia National Laboratories/Univ. of Oregon
TAU Performance System is a portable profiling and tracing toolkit for performance analysis of parallel programs written in Fortran, C, C++, Python, and Java.It is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements. All C++ language features are supported including templates and namespaces. The API also provides selection of profiling groups for organizing and controlling instrumentation. The instrumentation can be inserted in the source code using an automatic instrumentor tool based on the Program Database Toolkit (PDT), dynamically using DyninstAPI, at runtime using library preloading using tau_exec, and interpreter level instrumentation using Python, or even manually using the instrumentation API. TAU internally uses OTF2 to generate traces that may be visualized using the Vampir toolkit.
TAU Performance System is a portable profiling and tracing toolkit for performance analysis of parallel programs written in Fortran, C, C++, Python, and Java.It is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements. All C++ language features are supported including templates and namespaces. The API also provides selection of profiling groups for organizing and controlling instrumentation. The instrumentation can be inserted in the source code using an automatic instrumentor tool based on the Program Database Toolkit (PDT), dynamically using DyninstAPI, at runtime using library preloading using tau_exec, and interpreter level instrumentation using Python, or even manually using the instrumentation API. TAU internally uses OTF2 to generate traces that may be visualized using the Vampir toolkit.
2 p.m.
“High-Performance Multi-Mode X-ray Ptychography Reconstruction on Distributed GPUs” – Meifeng Lin, Brookhaven National Laboratory
X-ray ptychography is an important tool for reconstructing high-resolution specimen images from the scanning diffraction measurements. As an inverse problem, while there exists no unique solution to the ptychographical reconstructions, one of the best working approaches is the so-called difference map algorithm, based on which the illumination and object profiles are updated iteratively with the amplitude of their product constrained by the measured intensity at every iteration. Although this approach converges very fast (typically less than 100 iterations), it is a computationally intensive task and often requires several hours to retrieve the result on a single CPU, which is a disadvantage especially for beamline users who have limited access time. We accelerate this ptychography calculation by utilizing multiple GPUs and MPI communication. We take the scatter-and-gather approach by splitting the measurement data and sending each portion to a GPU node. Since data movement between the host and the device is expensive, the data is kept and the calculation is performed entirely on GPU, and only the updated probe and object are broadcasted at the end of each iteration. We show that our program has an excellent constant weak-scaling and enables users to obtain the results on the order of sub-minutes instead of hours, which is crucial for visualization, real-time feedback and efficient adjustment of experiments. This program is already put in production in the HXN beamline at NSLS-II, and a graphical user interface is also provided.
X-ray ptychography is an important tool for reconstructing high-resolution specimen images from the scanning diffraction measurements. As an inverse problem, while there exists no unique solution to the ptychographical reconstructions, one of the best working approaches is the so-called difference map algorithm, based on which the illumination and object profiles are updated iteratively with the amplitude of their product constrained by the measured intensity at every iteration. Although this approach converges very fast (typically less than 100 iterations), it is a computationally intensive task and often requires several hours to retrieve the result on a single CPU, which is a disadvantage especially for beamline users who have limited access time. We accelerate this ptychography calculation by utilizing multiple GPUs and MPI communication. We take the scatter-and-gather approach by splitting the measurement data and sending each portion to a GPU node. Since data movement between the host and the device is expensive, the data is kept and the calculation is performed entirely on GPU, and only the updated probe and object are broadcasted at the end of each iteration. We show that our program has an excellent constant weak-scaling and enables users to obtain the results on the order of sub-minutes instead of hours, which is crucial for visualization, real-time feedback and efficient adjustment of experiments. This program is already put in production in the HXN beamline at NSLS-II, and a graphical user interface is also provided.
3 p.m.
“Innovative Architectures for Experimental and Observational Science: Bringing Compute to the Data” – David Donofrio, Lawrence Berkeley National Laboratory
As the volume and velocity of data generated by experiments continues to increase, we find the need to move data analysis and reduction operations closer to the source of the data to reduce the burden on existing HPC facilities that threaten to be overrun by the surge of experimental and observational data. Furthermore, remote facilities, including astronomy observatories, particle accelerators such as SLAC LCLS-II, etc. producing data do not necessarily have dedicated HPC facilities on-site. These remote sites are often power or space constrained, making the construction of a traditional data center or HPC facility unreasonable. Further complicating these scenarios, each experiment often needs a blend of specialized and programmable hardware that is closely tied to the needs of the individual experiment. We propose a hardware generation methodology based on open-source components to rapidly design and deploy these data filtering and analysis computing devices. He re we demonstrate a potential near-sensor, real-time, data processing solution developed using an innovative open-source hardware generation technique allowing potentially more effective use of experimental devices, such as electron microscopy.
As the volume and velocity of data generated by experiments continues to increase, we find the need to move data analysis and reduction operations closer to the source of the data to reduce the burden on existing HPC facilities that threaten to be overrun by the surge of experimental and observational data. Furthermore, remote facilities, including astronomy observatories, particle accelerators such as SLAC LCLS-II, etc. producing data do not necessarily have dedicated HPC facilities on-site. These remote sites are often power or space constrained, making the construction of a traditional data center or HPC facility unreasonable. Further complicating these scenarios, each experiment often needs a blend of specialized and programmable hardware that is closely tied to the needs of the individual experiment. We propose a hardware generation methodology based on open-source components to rapidly design and deploy these data filtering and analysis computing devices. He re we demonstrate a potential near-sensor, real-time, data processing solution developed using an innovative open-source hardware generation technique allowing potentially more effective use of experimental devices, such as electron microscopy.
4 p.m.
“Co-design of Parallelware Tools by Appentra and ORNL: Addressing the Challenges of Developing Future Exascale HPC Applications” – Oscar Hernandez, Oak Ridge National Laboratory
The ongoing 4-year collaboration between ORNL and Appentra is enabling the development of new tools using the Parallelware technology to address the needs of leading HPC centers. Firstly, we will present Parallelware Trainer (https://www.appentra.com/products/parallelware-trainer/), a scalable interactive teaching environment tool for HPC education and training targeting OpenMP and OpenACC for multicores and GPUs. And secondly, we will present Parallelware Analyzer, a new command-line reporting tool aimed at improving the productivity of HPC application developers. The tool has been selected as an SC18 Emerging Technology, and we will demonstrate how it can help to facilitate the analysis of data layout and data scoping across procedure boundaries. The presentation with make enphasis on the new and upcoming technical features of the underlying Parallelware technology.
The ongoing 4-year collaboration between ORNL and Appentra is enabling the development of new tools using the Parallelware technology to address the needs of leading HPC centers. Firstly, we will present Parallelware Trainer (https://www.appentra.com/products/parallelware-trainer/), a scalable interactive teaching environment tool for HPC education and training targeting OpenMP and OpenACC for multicores and GPUs. And secondly, we will present Parallelware Analyzer, a new command-line reporting tool aimed at improving the productivity of HPC application developers. The tool has been selected as an SC18 Emerging Technology, and we will demonstrate how it can help to facilitate the analysis of data layout and data scoping across procedure boundaries. The presentation with make enphasis on the new and upcoming technical features of the underlying Parallelware technology.
Demo Station 2
11 a.m.
“Exciting New Developments in Large-Scale HPC Monitoring & Analysis” – Jim Brandt, Sandia National Laboratories
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
12 p.m.
“Accelerator Data Management in OpenMP 5.0” – Lingda Li, Brookhaven National Laboratory
Accelerator such as GPU play an essential role in today’s HPC systems. However, programming accelerators is often challenging. One of the most difficult part is to manage accelerator memory. OpenMP has supported accelerator offloading for a while and is gaining more and more usage. Currently, it requires users to explicitly manage memory mapping between host and accelerator, which needs a large amount of efforts from programmers. In OpenMP 5.0, user defined mapper and the support of unified memory are introduced to facilitate accelerator data management. We will introduce how to utilize these features to improve applications in this demo.
Accelerator such as GPU play an essential role in today’s HPC systems. However, programming accelerators is often challenging. One of the most difficult part is to manage accelerator memory. OpenMP has supported accelerator offloading for a while and is gaining more and more usage. Currently, it requires users to explicitly manage memory mapping between host and accelerator, which needs a large amount of efforts from programmers. In OpenMP 5.0, user defined mapper and the support of unified memory are introduced to facilitate accelerator data management. We will introduce how to utilize these features to improve applications in this demo.
1 p.m.
“In situ Visualization with SENSEI” – Burlen Loring, Lawrence Berkeley National Laboratory
In situ visualization and analysis is an important component of the path to exascale computing. Coupling simulation codes directly to analysis codes reduces their I/O while increasing the temporal fidelity of the analysis. SENSEI, a light weight in situ frame work, gives simulations access to a diverse set of analysis back ends through a simple API and data model. SENSEI currently supports ParaView Catalyst, VisIt Libsim, ADIOS, Python, and VTK-m based back ends and is easy to extend. In this presentation we introduce SENSEI and demonstrate its use with the IAMR an AMReX based compressible Navier Stokes simulation code.
In situ visualization and analysis is an important component of the path to exascale computing. Coupling simulation codes directly to analysis codes reduces their I/O while increasing the temporal fidelity of the analysis. SENSEI, a light weight in situ frame work, gives simulations access to a diverse set of analysis back ends through a simple API and data model. SENSEI currently supports ParaView Catalyst, VisIt Libsim, ADIOS, Python, and VTK-m based back ends and is easy to extend. In this presentation we introduce SENSEI and demonstrate its use with the IAMR an AMReX based compressible Navier Stokes simulation code.
2 p.m.
“On-line Memory Coupling of the XGV1 Code to ParaView and VisIt Using the ADIOS Software Framework” – Scott Klasky, Oak Ridge National Laboratory
The trends in high performance computing, where far more data can be computed that can ever be stored, have made on line processing techniques an important area of research and development. In this demonstration, we show on line visualization of data from XGC1, a particle-in-cell code used to study the plasmas in fusion tokamak devices. We use the ADIOS software framework for the on line data management and use the production tools, ParaView and VisIt for the visualization of the simulation data.
The trends in high performance computing, where far more data can be computed that can ever be stored, have made on line processing techniques an important area of research and development. In this demonstration, we show on line visualization of data from XGC1, a particle-in-cell code used to study the plasmas in fusion tokamak devices. We use the ADIOS software framework for the on line data management and use the production tools, ParaView and VisIt for the visualization of the simulation data.
3 p.m.
“Exciting New Developments in Large-Scale HPC Monitoring & Analysis” – Jim Brandt, Sandia National Laboratories
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
Wednesday, Nov. 14
Demo Station 1
10 a.m.
“In situ Visualization of a Multi-physics Simulation on LLNL’s Sierra Supercomputer” – Cyrus Harrison, Lawrence Livermore National Laboratory
11 a.m.
“In Situ Visualization with SENSEI” – Burlen Loring, Lawrence Berkeley National Laboratory
In situ visualization and analysis is an important component of the path to exascale computing. Coupling simulation codes directly to analysis codes reduces their I/O while increasing the temporal fidelity of the analysis. SENSEI, a light weight in situ frame work, gives simulations access to a diverse set of analysis back ends through a simple API and data model. SENSEI currently supports ParaView Catalyst, VisIt Libsim, ADIOS, Python, and VTK-m based back ends and is easy to extend. In this presentation we introduce SENSEI and demonstrate its use with the IAMR an AMReX based compressible Navier Stokes simulation code.
In situ visualization and analysis is an important component of the path to exascale computing. Coupling simulation codes directly to analysis codes reduces their I/O while increasing the temporal fidelity of the analysis. SENSEI, a light weight in situ frame work, gives simulations access to a diverse set of analysis back ends through a simple API and data model. SENSEI currently supports ParaView Catalyst, VisIt Libsim, ADIOS, Python, and VTK-m based back ends and is easy to extend. In this presentation we introduce SENSEI and demonstrate its use with the IAMR an AMReX based compressible Navier Stokes simulation code.
12 p.m.
“Real-time Performance Analysis of Applications and Workflow” – Gyorgy Matyasfalvi, Brookhaven National Laboratory
As part of the ECP CODAR project, Brookhaven National Laboratory, in collaboration with the Oregon Universities TAU team, has developed unique capabilities to analyze, reduce and visualize single application and complete workflow performance data in-situ. The resulting tool enables the researchers to examine and explore their workflow performance as it is being executed.
As part of the ECP CODAR project, Brookhaven National Laboratory, in collaboration with the Oregon Universities TAU team, has developed unique capabilities to analyze, reduce and visualize single application and complete workflow performance data in-situ. The resulting tool enables the researchers to examine and explore their workflow performance as it is being executed.
1 p.m.
“Real-time Performance Analysis of Applications and Workflow” – Gyorgy Matyasfalvi, Brookhaven National Laboratory
As part of the ECP CODAR project, Brookhaven National Laboratory, in collaboration with the Oregon Universities TAU team, has developed unique capabilities to analyze, reduce and visualize single application and complete workflow performance data in-situ. The resulting tool enables the researchers to examine and explore their workflow performance as it is being executed.
As part of the ECP CODAR project, Brookhaven National Laboratory, in collaboration with the Oregon Universities TAU team, has developed unique capabilities to analyze, reduce and visualize single application and complete workflow performance data in-situ. The resulting tool enables the researchers to examine and explore their workflow performance as it is being executed.
2 p.m.
“Charliecloud: LANL’s Lightweight Container Runtime for HPC” – Reid Priedhorsky, Los Alamos National Laboratory
Charliecloud provides user-defined software stacks (UDSS) for HPC centers. This “bring your own software stack” functionality addresses needs such as: software dependencies that are numerous, complex, unusual, differently configured, or simply newer/older than what the center provides; build-time requirements unavailable within the center, such as relatively unfettered internet access; validated software stacks and configuration to meet the standards of a particular field of inquiry; portability of environments between resources, including workstations and other test and development system not managed by the center; consistent environments, even archivally so, that can be easily, reliably, and verifiably reproduced in the future; and/or usability and comprehensibility. Charliecloud uses Linux user namespaces to run containers with no privileged operations or daemons and minimal configuration changes on center resources. This simple approach avoids most security risks while maintaining access to the performance and functionality already on offer. Container images can be built using Docker or anything else that can generate a standard Linux filesystem tree. We will present a brief introduction to Charliecloud, then demonstrate running portable Charliecloud containers of various flavors at native speed, including hello world, traditional MPI, data-intensive (e.g., Apache Spark), and GPU-accelerated (e.g., TensorFlow).
Charliecloud provides user-defined software stacks (UDSS) for HPC centers. This “bring your own software stack” functionality addresses needs such as: software dependencies that are numerous, complex, unusual, differently configured, or simply newer/older than what the center provides; build-time requirements unavailable within the center, such as relatively unfettered internet access; validated software stacks and configuration to meet the standards of a particular field of inquiry; portability of environments between resources, including workstations and other test and development system not managed by the center; consistent environments, even archivally so, that can be easily, reliably, and verifiably reproduced in the future; and/or usability and comprehensibility. Charliecloud uses Linux user namespaces to run containers with no privileged operations or daemons and minimal configuration changes on center resources. This simple approach avoids most security risks while maintaining access to the performance and functionality already on offer. Container images can be built using Docker or anything else that can generate a standard Linux filesystem tree. We will present a brief introduction to Charliecloud, then demonstrate running portable Charliecloud containers of various flavors at native speed, including hello world, traditional MPI, data-intensive (e.g., Apache Spark), and GPU-accelerated (e.g., TensorFlow).
3 p.m.
“Using Federated Identity to Improve the Superfacility User Experience” – Mark Day, Lawrence Berkeley National Laboratory
The superfacility vision combines multiple complementary user facilities into a virtual facility offering fundamentally greater capability than the standalone facilities provide on their own. For example, integrating beamlines at the Advanced Light Source (ALS), with HPC resources at NERSC via ESnet provides scientific capabilities unavailable at any single facility. This use of disparate facilities is not always convenient, and the logistics of setting up multiple user accounts, and managing multiple credentials adds unnecessary friction to the scientific process. We will demonstrate a simple portal, based on off-the-shelf technologies, that combines federated authentication with metadata collected at the time of the experiment and preserved at the HPC facility to allow a scientist to use their home institutional identity and login processes to access superfacility experimental data and results.
The superfacility vision combines multiple complementary user facilities into a virtual facility offering fundamentally greater capability than the standalone facilities provide on their own. For example, integrating beamlines at the Advanced Light Source (ALS), with HPC resources at NERSC via ESnet provides scientific capabilities unavailable at any single facility. This use of disparate facilities is not always convenient, and the logistics of setting up multiple user accounts, and managing multiple credentials adds unnecessary friction to the scientific process. We will demonstrate a simple portal, based on off-the-shelf technologies, that combines federated authentication with metadata collected at the time of the experiment and preserved at the HPC facility to allow a scientist to use their home institutional identity and login processes to access superfacility experimental data and results.
4 p.m.
“Innovative Architectures for Experimental and Observational Science: Bringing Compute to the Data” – David Donofrio, Lawrence Berkeley National Laboratory
As the volume and velocity of data generated by experiments continues to increase, we find the need to move data analysis and reduction operations closer to the source of the data to reduce the burden on existing HPC facilities that threaten to be overrun by the surge of experimental and observational data. Furthermore, remote facilities, including astronomy observatories, particle accelerators such as SLAC LCLS-II, etc. producing data do not necessarily have dedicated HPC facilities on-site. These remote sites are often power or space constrained, making the construction of a traditional data center or HPC facility unreasonable. Further complicating these scenarios, each experiment often needs a blend of specialized and programmable hardware that is closely tied to the needs of the individual experiment. We propose a hardware generation methodology based on open-source components to rapidly design and deploy these data filtering and analysis computing devices. He re we demonstrate a potential near-sensor, real-time, data processing solution developed using an innovative open-source hardware generation technique allowing potentially more effective use of experimental devices, such as electron microscopy.
As the volume and velocity of data generated by experiments continues to increase, we find the need to move data analysis and reduction operations closer to the source of the data to reduce the burden on existing HPC facilities that threaten to be overrun by the surge of experimental and observational data. Furthermore, remote facilities, including astronomy observatories, particle accelerators such as SLAC LCLS-II, etc. producing data do not necessarily have dedicated HPC facilities on-site. These remote sites are often power or space constrained, making the construction of a traditional data center or HPC facility unreasonable. Further complicating these scenarios, each experiment often needs a blend of specialized and programmable hardware that is closely tied to the needs of the individual experiment. We propose a hardware generation methodology based on open-source components to rapidly design and deploy these data filtering and analysis computing devices. He re we demonstrate a potential near-sensor, real-time, data processing solution developed using an innovative open-source hardware generation technique allowing potentially more effective use of experimental devices, such as electron microscopy.
Demo Station 2
10 a.m.
“Exciting New Developments in Large-scale HPC Monitoring & Analysis” – Jim Brandt, Sandia National Laboratories
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
11 a.m.
“Exciting New Developments in Large-scale HPC Monitoring & Analysis” – Jim Brandt, Sandia National Laboratories
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
This demonstration will provide a brief overview of a suite of HPC monitoring and analysis tools developed in collaboration between Sandia National Laboratories (SNL), Los Alamos National Laboratories (LANL), and Open Grid Computing (OGC). The demonstration will provide a highlight overview of: 1) our Lightweight Distributed Metric Service (LDMS) for data collection, transport, and storage (including use case examples of system and job based analyses with visualization) and 2) Baler tool for mapping log messages into patterns and performing a variety of pattern based analyses. For additional information about our suite of HPC monitoring and analysis tools please email ovis-help@sandia.gov or visit http://www.opengridcomputing.com/sc18
1 p.m.
“ECP SDK Software in HPC Container Environments” – Sameer Shende, Sandia National Laboratories/Univ. of Oregon
The ECP SDK project is providing software developed under the ECP project using Spack [http://www.spack.io] as the primary means of software distribution. Using Spack, we have also created container images of packaged ECP ST products in the Docker, Singularity, Shifter, and Charliecloud environments that may be deployed on HPC systems. This demo will show how to use these container images for software development and describe packaging software in Spack. These images will be distributed on USB sticks.
The ECP SDK project is providing software developed under the ECP project using Spack [http://www.spack.io] as the primary means of software distribution. Using Spack, we have also created container images of packaged ECP ST products in the Docker, Singularity, Shifter, and Charliecloud environments that may be deployed on HPC systems. This demo will show how to use these container images for software development and describe packaging software in Spack. These images will be distributed on USB sticks.
2 p.m.
“Spin: A Docker-based System at NERSC for Deploying Science Gateways Integrated with HPC Resources” – Cory Snavely, Lawrence Berkeley National Laboratory
This demonstration presents Spin, a Docker-based platform at NERSC that enables researchers to design, build, and manage their own science gateways and other services to complement their computational jobs, present data or visualizations produced by computational processes, conduct complex workflows, and more. After explaining the rationale behind building Spin and describing its basic architecture, staff will show how services can be created in just a few minutes using simple tools. A discussion of services implemented with Spin and ideas for the future will follow.
This demonstration presents Spin, a Docker-based platform at NERSC that enables researchers to design, build, and manage their own science gateways and other services to complement their computational jobs, present data or visualizations produced by computational processes, conduct complex workflows, and more. After explaining the rationale behind building Spin and describing its basic architecture, staff will show how services can be created in just a few minutes using simple tools. A discussion of services implemented with Spin and ideas for the future will follow.
4 p.m.
“ParaView Running on a Cluster” – W. Alan Scott, Sandia National Laboratories
ParaView will be running with large data on a remote cluster at Sandia National Laboratories.
ParaView will be running with large data on a remote cluster at Sandia National Laboratories.
Thursday, Nov. 15
Demo Station 1
10 a.m.
“SOLLVE: Scaling OpenMP with LLVM for Exascale Performance and Portability” – Sunita Chandrasekaran, Argonne National Laboratory/Oak Ridge National Laboratory
This demo will present an overview and some key software components of the SOLLVE ECP project. SOLLVE aims at enhancing OpenMP to cover the major requirements of ECP application codes. In addition, this project sets the goal to deliver a high-quality, robust implementation of OpenMP and project extensions in LLVM, an open source compiler infrastructure with an active developer community that impacts the DOE pre-exascale systems (CORAL). This project further develops the LLVM BOLT runtime system to exploit light-weight threading for scalability and facilitate interoperability with MPI. SOLLVE is also creating a validation suite to assess our progress and that of vendors to ensure that quality implementations of OpenMP are being delivered to Exascale systems. The project also encourages the accelerated development of similarly high-quality, complete vendor implementations and facilitate extensive interactions between the applications developers and OpenMP developers in industry.
This demo will present an overview and some key software components of the SOLLVE ECP project. SOLLVE aims at enhancing OpenMP to cover the major requirements of ECP application codes. In addition, this project sets the goal to deliver a high-quality, robust implementation of OpenMP and project extensions in LLVM, an open source compiler infrastructure with an active developer community that impacts the DOE pre-exascale systems (CORAL). This project further develops the LLVM BOLT runtime system to exploit light-weight threading for scalability and facilitate interoperability with MPI. SOLLVE is also creating a validation suite to assess our progress and that of vendors to ensure that quality implementations of OpenMP are being delivered to Exascale systems. The project also encourages the accelerated development of similarly high-quality, complete vendor implementations and facilitate extensive interactions between the applications developers and OpenMP developers in industry.