HIGH PERFORMANCE COMPUTING Monitoring and reporting tools allow
users to track and measure resource utilisation in workload managed clusters with comprehensive monitoring and reporting. Container support is available for
containers such as Docker or Singularity, in a Univa Grid Engine cluster and blend containers with other workloads supporting heterogeneous applications and technology environments. The software also provides GPU support to help users get the most out of GPU-powered servers by optimally mapping Machine Learning, HPC or other GPU-based workloads onto the topology of GPU servers in clusters or clouds.
some of the world’s most advanced supercomputing power and has been made possible by a strong collaborative initiative between NIWA and NeSI. The capabilities and potential have extended enormously since NIWA received the country’s first supercomputer almost 20 years ago,’ added Woods. ‘This facility is at the leading edge of
international science. This is a crucial resource for New Zealand science that will assist our researchers to seek solutions to some of today’s most urgent problems.’
Racing to increase HPC utilisation. The number of cloud-based HPC systems or hybrid cloud environments are increasing rapidly, as this allows users to consolidate HPC resources across multiple sites, and provides an easier way to manage these disparate systems. Univa, a company previously known for cluster provisioning software, has been making significant strides in cloud provisioning tools. One of the company’s main tools, Univa Grid Engine, is a batch-queuing system, derived from Sun Grid Engine. The software schedules resources in a datacentre and applies policy management tools. The product can be deployed to run on- premises, using cloud computing, or in a hybrid cloud. In October, Oracle announced that it had been working with SportPesa Racing Point
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F1 team to integrate Univa Grid Engine with the company’s computing infrastructure. The team has a computing infrastructure capped at 25 Tflops, so it is of the utmost importance that the resources can be utilised as efficiently as possible. It uses Grid Engine, to manage its CFD cluster, which is reported to deliver a sustained 97 per cent utilisation. Otmar Szafnauer, team principal and CEO, said: ‘Univa help us with bringing CFD developments to reality faster. So they help with efficiency with our compute power.’ Sportpesa Racing Point also utilises Grid
Engine, to ensure the correct simulations are running at the same time, provide the most efficient use of applications, and result in a quick turnaround of work throughput. ‘We wouldn’t be working with Univa if they
didn’t help us. Univa helped tremendously in making us more efficient in running CFD programs, getting accurate results and doing so very quickly. And because we can get accurate results quickly, that means it shortcuts our development of the car, and gets us quicker lap times sooner – and that’s invaluable in the sport, because that’s what it’s all about,’ added Szafnauer. Grid Engine software manages workload
placement automatically, maximises shared resources, provides enterprise-grade dependability and accelerates deployment of any container, application or service in any technology environment, on-premise or in the cloud.
Managing massive volumes of data Another recent case study from Univa highlights its work with Germany’s Bielefeld University, which aimed to make better use of the data generated by biotechnological activities and research projects at the university. The Center for Biotechnology (CeBiTec) constitutes one of the largest faculty- spanning central academic institutions. As part of CeBiTec, the Bioinformatics Resource Facility (BRF), led by Dr Stefan Albaum, provides a high-performance compute infrastructure accessible to its 150 members and all partner groups in the Faculties of biology, technology and chemistry, and more than 1,000 national and international researchers and affiliates from academia and industry.
The BRF was utilising an earlier version of Grid Engine for controlling access to compute resources. Increasing demand for high performance computing from CeBiTec’s researchers and partners meant workloads were no longer being processed efficiently. Generating enormous amounts of
experimental data places a heavy burden on the HPC clusters tasked with its storage, processing, visualisation and integration. For example, the assembly of the DNA sequence of an organism, based on the results from a high-throughput sequencer is highly RAM- demanding, as millions of very short DNA sequences are puzzled together to finally yield complete DNA sequences. These complex workloads were creating inefficient resource usage and bottlenecks. BRF selected Univa Grid Engine for its
optimised throughput and performance. ‘Right away, Univa Grid Engine enabled highly efficient usage of our compute resources with a very small footprint,’ said Dr Albaum. ‘We like the fact users who do not have experience can quickly submit jobs on the cluster. Univa is an established, easy-to- use system for managing the largest-scale processing of huge datasets. ‘Univa is an outstanding workload
orchestration solution for the distribution of large numbers of jobs on a compute cluster – even for heterogeneous set-ups like ours,’ concluded Dr Albaum.
December 2019/January 2020 Scientific Computing World 9
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