Frank Ham from Cascade Technologies presented this deck at the Stanford HPC Conference.
"A spin-off of the Center for Turbulence Research at Stanford University, Cascade Technologies grew out of a need to bridge between fundamental research from institutions like Stanford University and its application in industries. In a continual push to improve the operability and performance of combustion devices, high-fidelity simulation methods for turbulent combustion are emerging as critical elements in the design process. Multiphysics based methodologies can accurately predict mixing, study flame structure and stability, and even predict product and pollutant concentrations at design and off-design conditions."
Watch the video: http://insidehpc.com/2017/02/best-practices-large-scale-multiphysics/
Learn more: http://www.cascadetechnologies.com
and
http://www.hpcadvisorycouncil.com/events/2017/stanford-workshop/
Sign up for our insideHPC Newsletter: http:/insidehpc.com/newsletter
Designing HPC & Deep Learning Middleware for Exascale Systemsinside-BigData.com
DK Panda from Ohio State University presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI, PGAS (OpenSHMEM, CAF, UPC and UPC++) and Hybrid MPI+PGAS programming models by taking into account support for multi-core, high-performance networks, accelerators (GPGPUs and Intel MIC), virtualization technologies (KVM, Docker, and Singularity), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries will be presented."
Watch the video: http://wp.me/p3RLHQ-glW
Learn more: http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
In this video from the 2017 HPC Advisory Council Stanford Conference, Christian Kniep from Gaikai presents: Best Practices: State of Linux Containers.
"Linux Containers gain more and more momentum in all IT ecosystems. This talk provides an overview about what happened in the container landscape (in particular Docker) during the course of the last year and how it impacts datacenter operations, HPC and High-Performance Big Data. Furthermore Christian will give an update/extend on the ‘things to explore’ list he presented in the last Lugano workshop, applying what he learned and came across during the year 2016."
Watch the video: http://wp.me/p3RLHQ-glP
Learn more: http://qnib.org
and
http://www.hpcadvisorycouncil.com/events/2017/stanford-workshop/
Sign up for our insideHPC Newsletter: http:/insidehpc.com/newsletter
DSD-INT 2017 High Performance Parallel Computing with iMODFLOW-MetaSWAP - Ver...Deltares
Presentation by Jarno Verkaik (Deltares) at the iMOD International User Day, during Delft Software Days - Edition 2017. Tuesday, 31 October 2017, Delft.
Elastic multicore scheduling with the XiTAO runtimeMiquel Pericas
This presentation describes the XiTAO scheduler for heterogeneous computing that is currently under development in the EU LEGaTO project. The scheduler targets mixed-mode parallelism and assigns resource partitions just-in-time by creating a model of the platform's static and dynamic heterogeneity.
Frank Ham from Cascade Technologies presented this deck at the Stanford HPC Conference.
"A spin-off of the Center for Turbulence Research at Stanford University, Cascade Technologies grew out of a need to bridge between fundamental research from institutions like Stanford University and its application in industries. In a continual push to improve the operability and performance of combustion devices, high-fidelity simulation methods for turbulent combustion are emerging as critical elements in the design process. Multiphysics based methodologies can accurately predict mixing, study flame structure and stability, and even predict product and pollutant concentrations at design and off-design conditions."
Watch the video: http://insidehpc.com/2017/02/best-practices-large-scale-multiphysics/
Learn more: http://www.cascadetechnologies.com
and
http://www.hpcadvisorycouncil.com/events/2017/stanford-workshop/
Sign up for our insideHPC Newsletter: http:/insidehpc.com/newsletter
Designing HPC & Deep Learning Middleware for Exascale Systemsinside-BigData.com
DK Panda from Ohio State University presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI, PGAS (OpenSHMEM, CAF, UPC and UPC++) and Hybrid MPI+PGAS programming models by taking into account support for multi-core, high-performance networks, accelerators (GPGPUs and Intel MIC), virtualization technologies (KVM, Docker, and Singularity), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries will be presented."
Watch the video: http://wp.me/p3RLHQ-glW
Learn more: http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
In this video from the 2017 HPC Advisory Council Stanford Conference, Christian Kniep from Gaikai presents: Best Practices: State of Linux Containers.
"Linux Containers gain more and more momentum in all IT ecosystems. This talk provides an overview about what happened in the container landscape (in particular Docker) during the course of the last year and how it impacts datacenter operations, HPC and High-Performance Big Data. Furthermore Christian will give an update/extend on the ‘things to explore’ list he presented in the last Lugano workshop, applying what he learned and came across during the year 2016."
Watch the video: http://wp.me/p3RLHQ-glP
Learn more: http://qnib.org
and
http://www.hpcadvisorycouncil.com/events/2017/stanford-workshop/
Sign up for our insideHPC Newsletter: http:/insidehpc.com/newsletter
DSD-INT 2017 High Performance Parallel Computing with iMODFLOW-MetaSWAP - Ver...Deltares
Presentation by Jarno Verkaik (Deltares) at the iMOD International User Day, during Delft Software Days - Edition 2017. Tuesday, 31 October 2017, Delft.
Elastic multicore scheduling with the XiTAO runtimeMiquel Pericas
This presentation describes the XiTAO scheduler for heterogeneous computing that is currently under development in the EU LEGaTO project. The scheduler targets mixed-mode parallelism and assigns resource partitions just-in-time by creating a model of the platform's static and dynamic heterogeneity.
Userspace RCU library : what linear multiprocessor scalability means for your...Alexey Ivanov
RCU is well-known at the kernel-level for providing a way to synchronize shared data structures in read-often, update-rarely scenarios.
The development of a RCU library at the userspace application level has been mainly driven by the need for efficient synchronization of userspace tracing control data structures.
IBM kindly agreed to allow distribution of RCU-related code in a LGPL library, which makes it available for everyone to use. This can have large impact on the design of highly scalable applications performing caching of frequent requests, like domain name servers, proxy and web servers.
This presentation will discuss about the class of applications which could benefit from using the userspace RCU library.
The userspace RCU library is available under the LGPL license at http://www.lttng.org/urcu .
This session will walk us through the concept of orchestration and then it will explore orchestration solutions like Docker Swarm, K8 and Mesos + Marathon by helpful examples.
Low Energy Task Scheduling based on Work StealingLEGATO project
Abstract: Optimizing energy efficiency of parallel execution on computing systems, ranging from server farms, mobile devices to embedded systems, becomes increasingly one of the first-order concerns. A common way to express a parallel application is as a directed acyclic graph (DAG) in which each node represents a task. The problem of such task scheduling on multiprocessor systems is to find the proper execution processors. Especially nowadays asymmetric multiprocessor systems feature different type of cores with different performance and power consumption, e.g. Arm big.LITTLE and Intel Lakefield. However, naive task assignment without considering core types and task features could result in inefficient resources utilization and detrimentally impacts the overall energy consumption. Dynamic task scheduling is a widely used scheduling strategy, which does not require prior knowledge, e.g. architecture heterogeneity, task DAG structure, before execution but makes the decisions during runtime. Work stealing has been proven to be an effective method among dynamic task scheduling with better scalability in larger systems. DVFS is a common technique to achieve better energy efficiency, however, exploiting it costs reconfiguration overhead ranging from tens of microseconds to one millisecond. With fine-grained tasks as small as milliseconds, as required to expose large parallelism, it is not realistic to use DVFS on a per-task level. Also, it shows that the energy consumed in cores’ under-utilized period is significant.
Based on these problem statements, we come up with a low energy task scheduling work stealing runtime based on XiTAO where the system environment configurations are either fixed or managed by the O/S power governors or system administrators. The runtime contains dynamic performance tracing module, idleness tracing module, power profiling module and a task mapping algorithm. The dynamic performance model is able to give the accurate predictions for future tasks given a set of resources. It is independent of platforms and frequencies and achieves scalability and portability. Power profiling helps runtime systems to understand CPU power consumption trends with respect to number/type of cores and frequencies. Idleness tracing presents the real-time status of cores and contributes to the energy conservation of under-utilized period. It also provides the real-time parallel slackness of active cores, which allows the task mapping algorithm to attribute corresponding power consumption on each concurrent running task. The task mapping algorithm integrates the information from above three modules and outputs the predicted best resources placements for ready tasks.
Poster presented by jing Chen at the LEGaTO Final Event: 'Low-Energy Heterogeneous Computing Workshop'
Using Ceph for Large Hadron Collider DataRob Gardner
Talk by Lincoln Bryant (University of Chicago ATLAS team) on using Ceph for ATLAS data analysis @ Ceph Days Chicago http://ceph.com/cephdays/ceph-day-chicago/
Global routing topology.
loop-free paths . Dynamic distributed algorithm was rooted on this algorithm . Take Less Recovery Time(h) than previous algorithm (h^2) when Network Links fails .
Docker container virtualization provides an efficient way to create isolated scientific environments, adjusted and optimized for a specific problem or a specific group of users. It allows to efficiently separate responsibilities - with IT focusing on infrastructure for image repositories, preparation of basic images, container deployment and scaling, and physicists focusing on application development in environment of their choice.
Depending on demand, compute resources can be dynamically provisioned and a containerised scientific environment can be deployed in a matter of seconds on a user laptop, a batch farm, an HPC cluster or a cloud without need for a user to learn new environment, install additional libraries, resolve dependencies, recompile applications.
The present talk will describe DESY's experience with providing Docker service on our HPC cluster and report progress in using cloud to transparently and elastically extend containerised scientific environments - a work being done within the HELIX NEBULA Science Cloud project.
New Ceph capabilities and Reference ArchitecturesKamesh Pemmaraju
Have you heard about Inktank Ceph and are interested to learn some tips and tricks for getting started quickly and efficiently with Ceph? Then this is the session for you!
In this two part session you learn details of:
• the very latest enhancements and capabilities delivered in Inktank Ceph Enterprise such as a new erasure coded storage back-end, support for tiering, and the introduction of user quotas.
• best practices, lessons learned and architecture considerations founded in real customer deployments of Dell and Inktank Ceph solutions that will help accelerate your Ceph deployment.
40 Powers of 10 - Simulating the Universe with the DiRAC HPC Facilityinside-BigData.com
In this deck from the Swiss HPC Conference, Mark Wilkinson presents: 40 Powers of 10 - Simulating the Universe with the DiRAC HPC Facility.
"DiRAC is the integrated supercomputing facility for theoretical modeling and HPC-based research in particle physics, and astrophysics, cosmology, and nuclear physics, all areas in which the UK is world-leading. DiRAC provides a variety of compute resources, matching machine architecture to the algorithm design and requirements of the research problems to be solved. As a single federated Facility, DiRAC allows more effective and efficient use of computing resources, supporting the delivery of the science programs across the STFC research communities. It provides a common training and consultation framework and, crucially, provides critical mass and a coordinating structure for both small- and large-scale cross-discipline science projects, the technical support needed to run and develop a distributed HPC service, and a pool of expertise to support knowledge transfer and industrial partnership projects. The on-going development and sharing of best-practice for the delivery of productive, national HPC services with DiRAC enables STFC researchers to produce world-leading science across the entire STFC science theory program."
Watch the video: https://wp.me/p3RLHQ-k94
Learn more: https://dirac.ac.uk/
and
http://hpcadvisorycouncil.com/events/2019/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
DSD-NL 2017 Parallel Krylov Solver Package for iMODFLOW-MetaSWAP - VerkaikDeltares
Presentatie door Jarna Verkaik (Deltares) voor de iMOD NL Gebruikersdag, tijdens de Deltares Software Dagen- Editie 2017. Woensdag 14 juni 2017, Delft.
State of GeoServer provides an update on our community and reviews the new and noteworthy features for the Project. The community keeps an aggressive six month release cycle with GeoServer 2.8 and 2.9 being released this year.
Each releases bring together exciting new features. This year a lot of work has been done on the user interface, clustering, security and compatibility with the latest Java platform. We will also take a look at community research into vector tiles, multi-resolution raster support and more.
Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what these projects can do for you, this talk is for you.
DSD-INT 2023 Coupling Hydrologic Process Models - A technical perspective - R...Deltares
Presentation by Martijn Russcher (Deltares, Netherlands) at the Hydrology Suite User Days (Day 1) - Hydrology Suite introduction and River Basin Management software (RIBASIM), during the Delft Software Days - Edition 2023 (DSD-INT 2023). Tuesday, 28 November 2023, Delft.
Building a Multi-Region Cluster at Target (Aaron Ploetz, Target) | Cassandra ...DataStax
Lessons learned from a year spent building a Cassandra cluster over multiple regions, data centers, and providers. Will discuss our successes and learnings on replication, operations, and application development.
About the Speaker
Aaron Ploetz Lead Technical Architect, Target
Aaron is a Lead Technical Architect for Target, where he coaches development teams on modeling and building applications for Cassandra. He is active in the Cassandra tags on StackOverflow, and has also contributed patches to cqlsh. Aaron holds a B.S. in Management/Computer Systems from the University of Wisconsin-Whitewater, a M.S. in Software Engineering and Database Technologies from Regis University, and is a 2x DataStax MVP for Apache Cassandra.
Userspace RCU library : what linear multiprocessor scalability means for your...Alexey Ivanov
RCU is well-known at the kernel-level for providing a way to synchronize shared data structures in read-often, update-rarely scenarios.
The development of a RCU library at the userspace application level has been mainly driven by the need for efficient synchronization of userspace tracing control data structures.
IBM kindly agreed to allow distribution of RCU-related code in a LGPL library, which makes it available for everyone to use. This can have large impact on the design of highly scalable applications performing caching of frequent requests, like domain name servers, proxy and web servers.
This presentation will discuss about the class of applications which could benefit from using the userspace RCU library.
The userspace RCU library is available under the LGPL license at http://www.lttng.org/urcu .
This session will walk us through the concept of orchestration and then it will explore orchestration solutions like Docker Swarm, K8 and Mesos + Marathon by helpful examples.
Low Energy Task Scheduling based on Work StealingLEGATO project
Abstract: Optimizing energy efficiency of parallel execution on computing systems, ranging from server farms, mobile devices to embedded systems, becomes increasingly one of the first-order concerns. A common way to express a parallel application is as a directed acyclic graph (DAG) in which each node represents a task. The problem of such task scheduling on multiprocessor systems is to find the proper execution processors. Especially nowadays asymmetric multiprocessor systems feature different type of cores with different performance and power consumption, e.g. Arm big.LITTLE and Intel Lakefield. However, naive task assignment without considering core types and task features could result in inefficient resources utilization and detrimentally impacts the overall energy consumption. Dynamic task scheduling is a widely used scheduling strategy, which does not require prior knowledge, e.g. architecture heterogeneity, task DAG structure, before execution but makes the decisions during runtime. Work stealing has been proven to be an effective method among dynamic task scheduling with better scalability in larger systems. DVFS is a common technique to achieve better energy efficiency, however, exploiting it costs reconfiguration overhead ranging from tens of microseconds to one millisecond. With fine-grained tasks as small as milliseconds, as required to expose large parallelism, it is not realistic to use DVFS on a per-task level. Also, it shows that the energy consumed in cores’ under-utilized period is significant.
Based on these problem statements, we come up with a low energy task scheduling work stealing runtime based on XiTAO where the system environment configurations are either fixed or managed by the O/S power governors or system administrators. The runtime contains dynamic performance tracing module, idleness tracing module, power profiling module and a task mapping algorithm. The dynamic performance model is able to give the accurate predictions for future tasks given a set of resources. It is independent of platforms and frequencies and achieves scalability and portability. Power profiling helps runtime systems to understand CPU power consumption trends with respect to number/type of cores and frequencies. Idleness tracing presents the real-time status of cores and contributes to the energy conservation of under-utilized period. It also provides the real-time parallel slackness of active cores, which allows the task mapping algorithm to attribute corresponding power consumption on each concurrent running task. The task mapping algorithm integrates the information from above three modules and outputs the predicted best resources placements for ready tasks.
Poster presented by jing Chen at the LEGaTO Final Event: 'Low-Energy Heterogeneous Computing Workshop'
Using Ceph for Large Hadron Collider DataRob Gardner
Talk by Lincoln Bryant (University of Chicago ATLAS team) on using Ceph for ATLAS data analysis @ Ceph Days Chicago http://ceph.com/cephdays/ceph-day-chicago/
Global routing topology.
loop-free paths . Dynamic distributed algorithm was rooted on this algorithm . Take Less Recovery Time(h) than previous algorithm (h^2) when Network Links fails .
Docker container virtualization provides an efficient way to create isolated scientific environments, adjusted and optimized for a specific problem or a specific group of users. It allows to efficiently separate responsibilities - with IT focusing on infrastructure for image repositories, preparation of basic images, container deployment and scaling, and physicists focusing on application development in environment of their choice.
Depending on demand, compute resources can be dynamically provisioned and a containerised scientific environment can be deployed in a matter of seconds on a user laptop, a batch farm, an HPC cluster or a cloud without need for a user to learn new environment, install additional libraries, resolve dependencies, recompile applications.
The present talk will describe DESY's experience with providing Docker service on our HPC cluster and report progress in using cloud to transparently and elastically extend containerised scientific environments - a work being done within the HELIX NEBULA Science Cloud project.
New Ceph capabilities and Reference ArchitecturesKamesh Pemmaraju
Have you heard about Inktank Ceph and are interested to learn some tips and tricks for getting started quickly and efficiently with Ceph? Then this is the session for you!
In this two part session you learn details of:
• the very latest enhancements and capabilities delivered in Inktank Ceph Enterprise such as a new erasure coded storage back-end, support for tiering, and the introduction of user quotas.
• best practices, lessons learned and architecture considerations founded in real customer deployments of Dell and Inktank Ceph solutions that will help accelerate your Ceph deployment.
40 Powers of 10 - Simulating the Universe with the DiRAC HPC Facilityinside-BigData.com
In this deck from the Swiss HPC Conference, Mark Wilkinson presents: 40 Powers of 10 - Simulating the Universe with the DiRAC HPC Facility.
"DiRAC is the integrated supercomputing facility for theoretical modeling and HPC-based research in particle physics, and astrophysics, cosmology, and nuclear physics, all areas in which the UK is world-leading. DiRAC provides a variety of compute resources, matching machine architecture to the algorithm design and requirements of the research problems to be solved. As a single federated Facility, DiRAC allows more effective and efficient use of computing resources, supporting the delivery of the science programs across the STFC research communities. It provides a common training and consultation framework and, crucially, provides critical mass and a coordinating structure for both small- and large-scale cross-discipline science projects, the technical support needed to run and develop a distributed HPC service, and a pool of expertise to support knowledge transfer and industrial partnership projects. The on-going development and sharing of best-practice for the delivery of productive, national HPC services with DiRAC enables STFC researchers to produce world-leading science across the entire STFC science theory program."
Watch the video: https://wp.me/p3RLHQ-k94
Learn more: https://dirac.ac.uk/
and
http://hpcadvisorycouncil.com/events/2019/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
DSD-NL 2017 Parallel Krylov Solver Package for iMODFLOW-MetaSWAP - VerkaikDeltares
Presentatie door Jarna Verkaik (Deltares) voor de iMOD NL Gebruikersdag, tijdens de Deltares Software Dagen- Editie 2017. Woensdag 14 juni 2017, Delft.
State of GeoServer provides an update on our community and reviews the new and noteworthy features for the Project. The community keeps an aggressive six month release cycle with GeoServer 2.8 and 2.9 being released this year.
Each releases bring together exciting new features. This year a lot of work has been done on the user interface, clustering, security and compatibility with the latest Java platform. We will also take a look at community research into vector tiles, multi-resolution raster support and more.
Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what these projects can do for you, this talk is for you.
DSD-INT 2023 Coupling Hydrologic Process Models - A technical perspective - R...Deltares
Presentation by Martijn Russcher (Deltares, Netherlands) at the Hydrology Suite User Days (Day 1) - Hydrology Suite introduction and River Basin Management software (RIBASIM), during the Delft Software Days - Edition 2023 (DSD-INT 2023). Tuesday, 28 November 2023, Delft.
Building a Multi-Region Cluster at Target (Aaron Ploetz, Target) | Cassandra ...DataStax
Lessons learned from a year spent building a Cassandra cluster over multiple regions, data centers, and providers. Will discuss our successes and learnings on replication, operations, and application development.
About the Speaker
Aaron Ploetz Lead Technical Architect, Target
Aaron is a Lead Technical Architect for Target, where he coaches development teams on modeling and building applications for Cassandra. He is active in the Cassandra tags on StackOverflow, and has also contributed patches to cqlsh. Aaron holds a B.S. in Management/Computer Systems from the University of Wisconsin-Whitewater, a M.S. in Software Engineering and Database Technologies from Regis University, and is a 2x DataStax MVP for Apache Cassandra.
MayaData Datastax webinar - Operating Cassandra on Kubernetes with the help ...MayaData Inc
In this webinar experts from DataStax - the lead developer of Cassandra - and from MayaData - the lead developer of OpenEBS and LitmusChaos - will discuss and demonstrate ways to ensure the ease of use and resilience of Cassandra on Kubernetes.
Topics to be discussed and demonstrated include:
Provisioning underlying storage - how to make it consistent irrespective of the underlying hardware or cloud? Are there are ever reasons to have the storage replicate across nodes or is dynamic LocalPV the best choice in all cases?
Cass Operator - DataStax Kubernetes Operator for Apache Cassandra
Resilience - how to proactively assess the overall environment including the underlying Kubernetes with the help of Litmus
We leave in the era where the atomic building elements of silicon computers, e.g., transistors and wires, are no longer visible using traditional optical microscopes and their sizes are measured in just tens of Angstroms. In addition, power dissipation per unit volume is bounded by the laws of Physics that all resulted among others in stagnating processor clock frequencies. Adding more and more processor cores that perform simpler and simpler tasks in an attempt to efficiently fill the available on-chip area seems to be the current trend taken by the Industry.
Towards a Lightweight Multi-Cloud DSL for Elastic and Transferable Cloud-nati...Nane Kratzke
Cloud-native applications are intentionally designed for the cloud in order to leverage cloud platform features like horizontal scaling and elasticity – benefits coming along with cloud platforms. In addition to classical (and very often static) multi-tier deployment scenarios, cloud-native applications are typically operated on much more complex but elastic infrastructures. Furthermore, there is a trend to use elastic container platforms like Kubernetes, Docker Swarm or Apache Mesos. However, especially multi-cloud use cases are astonishingly complex to handle. In consequence, cloud-native applications are prone to vendor lock-in. Very often TOSCA-based approaches are used to tackle this aspect. But, these application topology defining approaches are limited in supporting multi-cloud adaption of a cloud-native application at runtime. In this paper, we analyzed several approaches to define cloud-native applications being multi-cloud transferable at runtime. We have not found an approach that fully satisfies all of our requirements. Therefore we introduce a solution proposal that separates elastic platform definition from cloud application definition. We present first considerations for a domain specific language for application definition and demonstrate evaluation results on the platform level showing that a cloud-native application can be transfered between different cloud service providers like Azure and Google within minutes and without downtime. The evaluation covers public and private cloud service infrastructures provided by Amazon Web Services, Microsoft Azure, Google Compute Engine and OpenStack.
Java Thread and Process Performance for Parallel Machine Learning on Multicor...Saliya Ekanayake
The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes.We focus on the special challenges of the Java Virtual Machine (JVM) using an Intel Haswell HPC cluster with 24 cores per node. Two parallel machine learning algorithms, K-Means clustering and Multidimensional Scaling (MDS) are used in our performance studies. We identify three major issues – thread models, affinity patterns, and communication mechanisms – as factors affecting performance by large factors and show how to optimize them so that Java can match the performance of traditional HPC languages like C. Further we suggest approaches that preserve the user interface and elegant dataflow approach of Flink and Spark but modify the runtime so that these Big Data frameworks can achieve excellent performance and realize the goals of HPCBig Data convergence.
Webinar: OpenEBS - Still Free and now FASTEST Kubernetes storageMayaData Inc
Webinar Session - https://youtu.be/_5MfGMf8PG4
In this webinar, we share how the Container Attached Storage pattern makes performance tuning more tractable, by giving each workload its own storage system, thereby decreasing the variables needed to understand and tune performance.
We then introduce MayaStor, a breakthrough in the use of containers and Kubernetes as a data plane. MayaStor is the first containerized data engine available that delivers near the theoretical maximum performance of underlying systems. MayaStor performance scales with the underlying hardware and has been shown, for example, to deliver in excess of 10 million IOPS in a particular environment.
State of GeoServer provides an update on our community and reviews the new and noteworthy features for the Project. The community keeps an aggressive six month release cycle with GeoServer 2.8 and 2.9 being released this year.
Each releases bring together exciting new features. This year a lot of work has been done on the user interface, clustering, security and compatibility with the latest Java platform. We will also take a look at community research into vector tiles, multi-resolution raster support and more.
Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what these projects can do for you, this talk is for you.
With the HPC Cloud facility, SURFsara offers self-service, dynamically scalable and fully configurable HPC systems to the Dutch academic community. Users have, for example, a free choice of operating system and software.
The HPC Cloud offers full control over a HPC cluster, with fast CPUs and high memory nodes and it is possible to attach terabytes of local storage to a compute node. Because of this flexibility, users can fully tailor the system for a particular application. Long-running and small compute jobs are equally welcome. Additionally, the system facilitates collaboration: users can share control over their virtual private HPC cluster with other users and share processing time, data and results. A portal with wiki, fora, repositories, issue system, etc. is offered for collaboration projects as well.
DSD-INT 2023 Hydrology User Days - Intro - Day 3 - KroonDeltares
Presentation by Timo Kroon and Nadine Slootjes (Deltares, Netherlands) at the Hydrology Suite User Days (Day 3) - Groundwater modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Thursday, 30 November 2023, Delft.
Presentation by Sabrina Couvin Rodriguez (Deltares, Netherlands) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
Presentation by Umit Taner (Deltares, Netherlands) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
Presentation by Daan Rooze (Deltares, Netherlands) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
DSD-INT 2023 Approaches for assessing multi-hazard risk - WardDeltares
Presentation by Philip Ward (Deltares and IVM VU Amsterdam) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
Presentation by Andrew Warren (Deltares, Netherlands) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
DSD-INT 2023 Global hydrological modelling to support worldwide water assessm...Deltares
Presentation by Marc Bierkens (Utrecht University and Deltares, Netherlands) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
DSD-INT 2023 Modelling implications - IPCC Working Group II - From AR6 to AR7...Deltares
Presentation by Bart van den Hurk (WGII Co-Chair, IPCC AR7, Deltares) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
DSD-INT 2023 Knowledge and tools for Climate Adaptation - JeukenDeltares
Presentation by Ad Jeuken (Deltares, Netherlands) at the Climate Adaptation Symposium 2023, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Wednesday, 29 November 2023, Delft.
DSD-INT 2023 Coupling RIBASIM to a MODFLOW groundwater model - BootsmaDeltares
Presentation by Huite Bootsma (Deltares, Netherlands) at the Hydrology Suite User Days (Day 3) - Groundwater modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Thursday, 30 November 2023, Delft.
DSD-INT 2023 Create your own MODFLOW 6 sub-variant - MullerDeltares
Presentation by Mike Muller (hydrocomputing GmbH & Co. KG, Germany) at the Hydrology Suite User Days (Day 3) - Groundwater modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Thursday, 30 November 2023, Delft.
DSD-INT 2023 Example of unstructured MODFLOW 6 modelling in California - RomeroDeltares
Presentation by Betsy Romero Verástegui (Deltares, Netherlands) at the Hydrology Suite User Days (Day 3) - Groundwater modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Thursday, 30 November 2023, Delft.
DSD-INT 2023 Challenges and developments in groundwater modeling - BakkerDeltares
Presentation by Mark Bakker (Delft University of Technology, Netherlands) at the Hydrology Suite User Days (Day 3) - Groundwater modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Thursday, 30 November 2023, Delft.
DSD-INT 2023 Demo new features iMOD Suite - van EngelenDeltares
Presentation by Joeri van Engelen (Deltares, Netherlands) at the Hydrology Suite User Days (Day 3) - Groundwater modelling, during the Delft Software Days - Edition 2023 (DSD-INT 2023). Thursday, 30 November 2023, Delft.
DSD-INT 2023 iMOD and new developments - DavidsDeltares
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DSD-INT 2023 Parameterization of a RIBASIM model and the network lumping appr...Deltares
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Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
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AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
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Venez le découvrir lors de cette session ignite
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Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
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The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
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SOCRadar Research Team: Latest Activities of IntelBroker
DSD-INT 2019 Parallelization project for the USGS - Verkaik
1. i M O D U s e r D a y 2 0 1 9 – D S D - I N T 2 0 1 9
Parallelization project for the USGS
Jarno Verkaik (Deltares, groundwater management department)
SURFsara Cartesius supercomputer
(47,776 cores, 130TB RAM)
4. How it started…
• 2010: Email correspondence on parallel MT3DMS
• 2013: Visit to USGS, start of joined code development (in kind)
• 2015: Start development of Parallel Krylov Solver for
MODFLOW-2005 and MODFLOW-USG
→ poster @ AGU Fall Meeting 2015, San Francisco
• 2016: First application of PKS at national and global scale
→ poster @ AGU Fall Meeting 2016, San Francisco
• Jul.2017: PKS as main feature for iMOD 4.0
& applied as default solver in National Water Model
• Oct.2017: Start parallelization of MODFLOW 6
→ funded by USGS through USGS-Deltares co-op
iMODUserDay2019–DSD-INT2019
4
AGU Fall Meeting 2015
AGU Fall Meeting 2016
5. Organization through (coastal morphology) USGS-Deltares co-op
iMODUserDay2019–DSD-INT2019
5
Robert McCall
Applied Morphodynamics,
Delft
Kees Nederhoff
Deltares USA,
Silver Spring
Martijn Russcher
Numerical Simulation Software,
Delft
Jarno Verkaik
Groundwater management,
Utrecht
Joseph D. Hughes
Integrated Modeling and Prediction,
Reston
Christian D. Langevin
Integrated Modeling and Prediction,
Mounds View
Li Erikson
Pacific Coastal and Marine
Science Center, Santa Cruz
6. USGS project FY2018 (Oct.2017 – Sep.2018)
• Start parallelization of MODFLOW 6
• Such that it can be part of a future release
• Target application: CONUS model by
Wesley Zell and Ward Sanford (USGS)
• USGS requirements:
- Proof of concept applicable to CONUS model
- Low code footprint
- Version controlled code at GitHub
- Easy to use
- Not depending on 3rd party libraries
iMODUserDay2019–DSD-INT2019
6
7. USGS project FY2018 (Oct.2017 – Sep.2018)
• Proof of concept was developed, applicable to CONUS
• Parallelization of IMS linear solver using Schwarz domain decomposition
(similar to Parallel Krylov Solver in iMOD)
• Repos: https://github.com/verkaik/modflow6-parallel.git
→MODFLOW 6 framework refactoring required for
exchanges between models (subdomains):
- That is generic for both serial and parallel computing
- Such that numerical schemes can be evaluated more easily at model interfaces
- Such that XT3D option can be used with multiple models (serial and parallel)
iMODUserDay2019–DSD-INT2019
7
Halo v2. concept
8. USGS project FY2019 & FY2020
• FY2019 (Oct.2018 – Sep.2019)
• Support XT3D option with multi-models (serial only)
• Development of interface model concept (revised halo v2)
• FY2020 (Oct.2019 – Sep.2020)
(To be determined)
• Continue working on parallel MODFLOW
• Development of Basic Model Interface
iMODUserDay2019–DSD-INT2019
8
M1
M2
9. USGS project FY2018 results: circle test 1250M cells
iMODUserDay2019–DSD-INT2019
9
10. USGS project FY2018 results: circle test 12.5M cells
iMODUserDay2019–DSD-INT2019
10
11. Related work to USGS project
iMODUserDay2019–DSD-INT2019
• PhD project (start 2018)
“Towards Exascale Computing for Large Scale Groundwater Simulation”
Goal: development of distributed parallel methods applying to large real-life groundwater models of
O(106)–O(109) cells.
• Mainly funded by Deltares research
• Directly relates to MODFLOW 6 kernel development for new iMOD 6
(see next presentation by Otto de Keizer)
Prof. Marc Bierkens
(Utrecht University)
Prof. Hai Xiang Lin
(Delft University of Technology)
Gualbert Oude Essink, PhD
(Deltares)
11
12. Contributions from PhD project
iMODUserDay2019–DSD-INT2019
Short term coding:
• Improve linear solver convergence when using many subdomains:
→ add coarse grid parallel preconditioner (implementation largely done)
• Option to check parallel implementation
→ add serial block Jacobi preconditioner (first implementation done)
• Code profiling & optimizing parallel performance (ongoing)
Longer term coding:
• Robustness option when using many subdomains:
→ add recovery mechanism for failing hardware
• Add physics-based parallel preconditioner
Short term modeling:
• Run USGS CONUS model in parallel @ 250 m 12
13. • Development of PCR-GLOBWB global groundwater model
having 1km x 1km resolution, O(108) cells
• First experience with parallel MODFLOW 6 with this scale:
• Physics based subdomain partitioning
• Model generation (pre-processing)
• Parallel computing
• Visualization of model results
→ Big data!
Typical raster: 43200 columns x 21600 rows, 3 GB binary
Global groundwater model @ 1km and 428M cells
Ref: Verkaik, J., Sutanudjaja, E.H., Oude Essink, G.H.P., Lin, H.X., and Bierkens, M.F.P., 2019. Parallel global hydrology and water resources
PCR-GLOBWB-MODFLOW model at hyper-resolution scale (1 km): first results, in: EGU General Assembly Conference Abstracts. p. 13397.
iMODUserDay2019–DSD-INT2019
13
14. MODFLOW 6 model characteristics:
• Steady-state, 2 layers, subsurface down-scaled from 10km
• Unstructured DISU grid met only “land cells”, total 428M
• CHD for sea, RIV in layer 1 + DRN in layer 1 & 2 (HydroSHEDS)
Global groundwater model @ 1km and 428M cells
iMODUserDay2019–DSD-INT2019
Parallel pre-processing using 128 subdomains
14
15. Global groundwater model @ 1km and 428M cells
Can we come up with predefined subdomain boundaries (e.g. hydrologically / administrative
boundary) such that they are useful for both modeler and parallel computing?
→ How to partition the world into 1024 subdomains using 1.8M catchments?
→ How to solve a sub-optimal optimization problem (load + edge cuts)?
1. Determine independent regions for groundwater
flow (continents, islands)
→ ~20k regions
2. Further divide large regions/catchments using
a lumped graph method → define parallel models
3. Cluster small regions → define serial models
iMODUserDay2019–DSD-INT2019
15
16. Global groundwater model @ 1km and 428M cells
• Partitioning results in 52 separate MODFLOW 6 models:
• 38 serial, small islands
• 13 parallel, 3 largest on super computer
428M
(2 layers)
Small parallel+
serial models
2. America
120M cells
286 cores
1min 36sec
112GB memory
1. Africa+EurAsia
256M cells
612 cores
3min 31sec
390 GB memory
3. Australia
20M cells
48 cores
33 sec
13 GB memory
5%
28%
60%
iMODUserDay2019–DSD-INT2019
16
17. Global groundwater model @ 1km and 428M cells
iMODUserDay2019–DSD-INT2019
Simulated
Groundwater Table
subdomain
boundary
(total: 1024)
17
18. Global groundwater model @ 1km and 428M cells
iMODUserDay2019–DSD-INT2019
Simulated
Groundwater Table
subdomain
boundary
(total: 1024)
Take home message:
USGS and Deltares are making progress on MPI parallelization
of the MODFLOW 6 multi-model capability
for reducing computing times & memory usage
THANK YOU! 18