A mere six months ago Paul Lajbcygier and his research group used R@CMon Phase 2 “specialist kit” for processing and analysing higher frequency stock data, as part of their stock price impact models study. Since then, they’ve been running extraction queries continuously and recently published a paper highlighting their latest findings while acknowledging the NeCTAR Research Cloud infrastructure.
The group will continue to use the high-memory instance on R@CMon Phase 2 as they progress their research pipeline and the R@CMon team will continue to support them on their journey.
“I expect that over the coming months we will fully utilise the generous resources on the Monash node of the NeCTAR Research Cloud as we extend our research into this cutting edge and exciting data intensive topic.”
Associate Professor Paul Lajbcygier
Faculty of Business and Economics
Department of Accounting and Finance
Department of Banking and Finance
Monash University
Monash University, through the Institute of Railway Technology (IRT), has been working on a research project with Vale S.A., a Brazilian multinational metals and mining corporation and one of the largest logistical operators in Brazil, to continuously monitor and assess the health of the Carajás Railroad Passenger Train (EFC) mixed-use rail network in Northern Brazil. This project will identify locations that produce “significant dynamic responses” with the aim for proactive maintenance to prevent catastrophic rail failure. As a part of this project, IRT researchers have been involved in (a) the analysis of the collected data and (b) the establishment of a database with visualisation capabilities that allows for the interrogation of the analysed data.
GPU-powered DataMap analysis and visualisation on R@CMon.
Researchers use the DataMap analysis software for data interrogation and visualisation. DataMap is a Windows-based client-server tool that integrates data from various measurements and recording systems into a geographical map. Traditionally they have the software running on a commodity laptop with a dedicated GPU connecting to their database server. To scale to larger models, conduct more rigorous analysis and visualisation, as well as support remote collaboration, the system of tools needed to go beyond the laptop.
The R@CMon team supported IRT in deploying the software on the NeCTAR Research Cloud. The deployed instance runs on the Monash-licensed Windows flavours with GPU-passthrough to support DataMap’s DirectX requirements.
GPU-powered DataMap analysis and visualisation on R@CMon.
Through the Research Cloud IRT researchers and Vale S.A. counterparts are able to collaborate for modelling, analysis and results using remote access to the GPU-enabled virtual machines.
“The assistance of R@CMon in providing virtual machines that have GPU support, has been instrumental in facilitating global collaboration between staff located at Vale S.A. (Brazil) and Monash University (Australia).”
Yesterday Mellanox made the following press release – “Australia’s Largest University Selects Mellanox CloudX Platform and Open Ethernet Switch Systems for Nationwide Research Initiative“. Through Monash University’s own co-investment into R@CMon, the Mellanox Cloudx products were chosen as the networking technology to Phase 2, providing RDMA capable networking within and between R@CMon Research Cloud and Data (RDSI) facilities. This means our one fabric can run multi-host MPI workloads, and leverage fast I/O storage, but also remain near the cost-point of commodity networking for the resources that are generic and commodity.
This is a key ingredient to the “21st Century Microscope”, where researchers orchestrate the instruments, compute, storage, analysis and visualisation themselves, looking down and tuning this 21st century lens, using big data and big computing to make new discoveries. R@CMon has been designed to be the platform where Australian researchers can lead the way at establishing their own 21st century microscope – for themselves and for their communities.
Once again Monash is leading platform technology innovation and accessibility by example. Through 2015 we look forward to optimising this technology, and encouraging increased self-service to these sorts of technologies.
Monash is home to the Multi-modal Australian ScienceS Imaging and Visualisation Environment (MASSIVE), a national facility for the imaging and characterisation community. An important and rather novel feature of the MASSIVE compute cluster is the interactive desktop visualisation environment available to assist users in the characterisation process. The MASSIVE desktop environment provided part of the inspiration for the Characterisation Virtual Laboratory (CVL), a NeCTAR VL project combining specialist software visualisation and rendering tools from a variety of disciplines and making them available on and through the NeCTAR research cloud.
The recently released monash-02 zone of the NeCTAR cloud provides enhanced capability to the CVL, bringing a critical mass of GPU accelerated cloud instances. monash-02 includes ten GPU capable hypervisors, currently able to provide up to thirty GPU accelerated instances via direct PCI passthrough. Most of these are NVIDIA GRID K2 GPUs (CUDA 3.0 capable), though we also have one K1. Special thanks to NVIDIA for providing us with a couple of seed units to get this going and supplement our capacity! After consultation with various users we created the following set of flavors/instance-types for these GPUs:
Flavor name
#vcores
RAM (MB)
/dev/vda (GB)
/dev/vdb (GB)
mon.r2.5.gpu-k2
1
5400
30
N/A
mon.r2.10.gpu-k2
2
10800
30
40
mon.r2.21.gpu-k2
4
21700
30
160
mon.r2.63.gpu-k2
12
65000
30
320
mon.r2.5.gpu-k1
1
5400
30
N/A
mon.r2.10.gpu-k1
2
10800
30
40
mon.r2.21.gpu-k1
4
21700
30
160
mon.r2.63.gpu-k1
12
65000
30
320
R@CMon has so far dedicated two of these GPU nodes to the CVL, and this is our preferred method for use of this equipment, as the CVL provides a managed environment and queuing system for access (regular plain IaaS usage is available where needed). There were some initial hiccups getting the CVL’s base CentOS 6.6 image working with NVIDIA drivers on these nodes, solved by moving to a newer kernel, and some performance tuning tasks still remain. However, the CVL has now been updated to make use of the new GPU flavors on monash-02, as demonstrated in the following video…
GPU-accelerated Chimera application running on the CVL, showing the structure of human follicle-stimulating hormone (FSH) and its receptor.
If you’re interested in using GPGPUs on the cloud please contact the R@CMon team or Monash eResearch Centre.
