by Seth Feder
Genomics is no longer solely the domain of university research labs and clinical trials. Commercial entities such as tertiary care hospitals, cancer centers, and large diagnostics labs are now sequencing genomes. Perhaps ahead of the science, consumers are seeing direct marketing messages about genomic tumor assessments on TV. Not surprising, venture capitalists are looking for their slice of the pie, last year investing approximately $248 million in personalized medicine startups.
So how can health IT professionals get involved? As in the past, technology coupled with innovation (and the right use-case) can drive new initiatives to widespread adoption. In this case, genomic medicine has the right use-case and IT innovation is driving adoption.
While the actual DNA and RNA sequencing takes place inside very sophisticated instrumentation, sequencing is just one step in the process. The raw data has to be processed, analyzed, interpreted, reported, shared, and then stored for later use. Sound familiar? It should, because we have seen this before in such fields as digital imaging which drove the wide spread deployment of Picture Archiving and Communicating Systems (PACS) in just about every hospital and imaging clinic around the world.
As in PACS, those in clinical IT must implement, operationalize, and support the workflow. The processing and analysis of genomic data is essentially a big data problem, solved by immense amounts of computing power. In the past, these resources were housed inside large exotic supercomputers only available to elite institutions. But today HPC built on scale-out x86 architectures with multi core processors have made this power attainable to the masses – and thus democratized. Parallel file systems that support HPC are much easier to implement and support, as are standard high bandwidth InfiniBand and Ethernet networks. Further, public cloud is emerging as a supplement to on-premise computing power. Some organizations are exploring off-loading part of the work beyond their own firewall, either for added compute resources or as a location for long term data storage.
For example, in 2012 myself and others at Dell worked with the Translational Genomics Research Institute (TGen) to tune its system for genomics input/output demands by scaling its existing HPC cluster to include more servers, storage and networking bandwidth. This allowed researchers to get the IT resources they needed faster without having to depend on shared systems. TGen worked with the Neuroblastoma and Medulloblastoma Translational Research Consortium (NMTRC) to develop methodology for fast sequencing of childhood cancer tumors, allowing NMTRC doctors to quickly identify appropriate treatments for young patients.
You can now get pre-configured HPCs to work with genomic software toolsets, which enabled clinical and translational research centers like TGen to do large-scale sequencing projects. The ROI and price per performance is compelling for anyone doing heavy genomic workloads. Essentially, with one rack of gear, any clinical lab now has all the compute power needed to process and analyze multiple genome sequences per day, which is a clinically relevant pace.
Genomic medicine is here, and within a few years will become standard care to sequence many diseases in order to determine proper treatment. As the science advances, the HPC community will be ready contribute in making this a reality. You can learn more here.
by Tom Raisor
The San Diego Supercomputer Center (SDSC) at the University of California, San Diego has transitioned into the early operations stages of its new Comet supercomputer. When it is fully functioning, the new cluster will have an overall peak performance approaching two petaflops.
Comet has been designed as a solution for the "long tail" of science, which refers to the significant amount of research that is computationally-based, but modest-sized. Together, these projects represent a great amount of research and potential scientific impact. Much of this research is being conducted in disciplines that are new to high performance computing such as economics, genomics and social sciences.
The Comet cluster includes:
You can learn more about Comet and its mission to serve the long tail of science here.
Having the ability to quickly and effectively react to customer needs and market demands is invaluable to a business. Yet too many decision makers are stymied by a lack of useful insight into their data. However, agility and efficacy in analytics is possible. With the right mindset, tools and technologies, organizations can become much more adroit about how they use the power of analytics to improve decision making.
A recent survey indicated that an impressive 61% of organizations around the globe have data waiting to be processed. Unfortunately, a mere 39% felt they understood how to extrapolate the value from that data.
In order to unlock the value found in data, organizations must have:
The analytics tools needed to drive fast and flexible business decisions are available. However, it also takes the right mindset for the power of analytics to improve decision making.
You can read more about what IT decision makers are thinking about a variety of data-related topics here.
When it comes to processing big data platforms, Hadoop has become the go-to platform. It allows vast amounts of data, especially unstructured or very diverse data, to be quickly processed. As the de facto open sources parallel file system for HPC environments, Lustre provides compute clusters with efficient storage and fast access to large data sets. Together these technologies help to solve big data problems. However, they also present some disadvantages, including a need for HTTP calls, added overhead, reduced efficiency, slower speed, and a requirement for fairly large local storage on each Hadoop node.
There is, however, a way to overcome those obstacles. As a Hadoop software adaptor, Intel Enterprise Edition for Lustre (IEEL) provides direct access to Lustre during MapReduce computations, improving performance.
A presentation by J. Mario Gallegos, at the Recent LUG 15 conference highlighted some of the advantages gained and some of the best practices to follow when adding IEEL.
Among the advantages observed:
You can read about Mario's other findings and see his LUG presentation here.
by Ashish Kumar Singh
This blog explores performance analysis of WRF (Weather Research and Forecasting) model on a cluster of PowerEdge R730 servers with Intel Xeon Phi 7120Ps Coprocessors. All the runs were carried out with Hyper Threading (logical Processors) disabled.
The WRF (Weather Research and Forecasting) model is a next-generation mesoscale numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. The model serves a wide range of metrological applications across scales from tens of meters to thousands of kilometers. WRF allows for atmospheric simulations based on real data (observations, analysis) or idealized conditions to be generated.
Test Cluster Configuration:
The test cluster consisted of four PowerEdge R730 servers with two Intel Xeon Phi 7120P co-processors each. Each PowerEdge R730 had two Intel Xeon E5-2695v3 @ 2.3GHz CPU and eight 16GB DIMMS of 2133MHz making it a total of 128GB of memory. Each PowerEdge R730 consisted of one Mellanox FDR Infiniband HCA card in the low-profile x8 PCIe Gen3 slot (Linked with CPU2).
Compute node configuration
The BIOS options selected for this blog were as below:
WRF performance analysis was run for Conus-2.5km data. The Conus-2.5km data set was a single domain,the large size 2.5KM is equal to the continental US, which had the final 3hr simulation for hours 3-6, starting from a provided restart file. It may also be performed for the full 6hrs starting from a cold start.
All the runs on CPU with Intel Xeon Phi configuration were performed in symmetric mode. For single node CPUs-only configuration, the average time was 7.425 seconds. However on CPUs and two Intel Xeon Phi configurations, the average time taken was 6.093 seconds, which showed improvement of 1.2 times. With a two node cluster of CPUs and Intel Xeon Phi, the average time was 2.309 seconds, an improvement of 3.2 times. For a four node cluster of CPUs and Intel Xeon Phi configuration, a performance improvement was increased to 5.7 times.
The power consumption analysis for WRF with Conus-2.5KM benchmark is shown below. On single node, with CPU only configuration, the power consumption was 395.4 watts. On CPUs with one Intel Xeon Phi configuration, power consumption was at 526.3 watts, while on CPUs with two Intel Xeon Phi configuration, the power consumption was 688.2 watts.
Results showed power consumption increase in addition of Intel Xeon Phi. However, results also showed increase in performance per watt to the order of 2.6 times on a CPUs with two Intel Xeon Phi configuration.
The configuration of CPUs with Intel Xeon Phi 7120P showed sustained performance and power-efficiency gains in comparison to CPUs-only configuration. With two Intel Xeon Phi 7120Ps WRF with Conus-2.5KM benchmark showed 1.2 fold increase and performance per watt improved by more than 2.6 times too, resulting in a powerful, easy-to-use and energy efficient HPC platform.
This blog explores the application performance analysis of NAMD (NAnoscale Molecular Dynamics) for large data sets on cluster of PowerEdge R730 servers with Intel Xeon Phi 7120Ps. All the runs were carried out with Hyper Threading (logical processors) disabled. IB verbs version of NAMD was used for all the runs.
The test cluster consisted of four PowerEdge R730 servers with two Intel Xeon Phi 7120P co-processors each. Each PowerEdge R730 had two Intel Xeon E5-2695v3 @ 2.3GHz CPU and eight 16GB DIMMS of 2133MHz making it a total of 128GB of memory per server. Each PowerEdge R730 consisted of one Mellanox FDR Infiniband HCA card in the low-profile x8 PCIe Gen3 slot (Linked with CPU2).
Compute node configuration
The BIOS options selected for this blog are as below:
NAMD (NAnoscale Molecular Dynamics) is a parallel, object-oriented simulation package written using the Charm++ parallel programming model, designed for high performance simulation of large bimolecular systems. Charm++ is developed with simplified parallel programming and also provides automatic load balancing, which is crucial to the performance of NAMD.
All the runs with STMV (virus) benchmark were run with ibverbs version of NAMD. The performance analysis with STMV benchmark shown below. STMV (Satellite Tobacco Mosaic Virus) is a small, icosahedral plant virus. On single node, we observed performance improvement of 2.5 times on CPUs with Intel Xeon Phi configuration in comparison to CPUs-only configuration.
STMV showed performance of 0.2ns/day with CPUs-only configuration. With CPUs and two Intel Xeon Phi performance was 0.5ns/day, which showed performance increase of 2.5 times. While on a four node cluster with the CPUs and Intel Xeon Phi 7120P performance increase was 8.5 times. Scaling from one node to four node resulted in almost 3.5 times scale-up.
The Power analysis was done for single node among CPUs-only configuration, CPUs with one Intel Xeon Phi 7120P configuration and CPUs with two Intel Xeon Phi 7120P configuration. With CPUs and two Intel Xeon Phi configuration, the power consumption increased along with the performance per watt, which was 2.4 times in comparison to CPU-only configuration. The power efficiency increase showed in below picture.
With CPUs and two Intel Xeon Phi 7120Ps, the STMV benchmark demonstrated increase of 2.5 times in performance and 2.4 times in power efficiency when compared to CPUs-only configuration, resulting in a powerful and energy efficient HPC platform.
This blog explores the HPL (High Performance LINPACK) performance and power analysis on Intel Xeon Phi 7120P cluster with current generation PowerEdge R730 servers. All the runs were carried out with Hyper Threading (logical Processors) disabled.
Compute node configuration
High Performance LINPACK is a benchmark that solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed memory systems. HPL performed with block size of NB=192 for CPU only and NB=1280 for Intel Xeon Phi (offload) with different problem sizes of N=118272 (NB=1280) for single node N=172032 (NB=1280) for two node and N=215040 (NB=1280) for four node cluster runs.
Compared to the Intel CPU only configuration, the acceleration was about 3 times with Intel Xeon Phi 7120Ps.
On a single node, with CPUs only, the PowerEdge R730 achieved 802.09 GFOLPS, while with two 7120Ps it was 2.553 TFLOPS. So the 7120P provides 3.26X performance increase. Similarly, two node and four node demonstrated performance increase of 3.25X.
The HPL power consumption analysis is shown among CPU only, CPU with one Intel Xeon Phi and CPU with two Intel Xeon Phi.
The power consumption of single node CPUs-only was about 398.72 watts. With two 7120Ps and CPUs, it was increased to 983.5 watts. It showed the power consumption of the CPUs-only configuration was lower than system with Intel Xeon Phi. while the performance per watt for the configurations with Intel Xeon Phi was 1.31 times of CPUs-only configuration.
The Intel Xeon Phi 7120P showed sustained performance and power-efficiency gains in comparison to CPUs only. With two Intel Xeon Phi 7120Ps, HPL benchmark showed three fold performance increase in comparison to CPUs only and the performance per watt was improved by more than one fold, resulting in a powerful and energy efficient HPC platform.
This blog explores the application performance analysis of LAMMPS on a cluster of PowerEdge R730 servers with Intel Xeon Phi 7120Ps. All the runs were carried out with Hyper Threading (logical processors) disabled.
LAMMPS (Large Scale Atomic/Molecular Massively Parallel Simulator) is a classical molecular dynamics code, capable of doing simulation for solid-state materials (metals, semi-conductors), soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model atoms or more generically as a parallel particle simulator at the atomic, meso or continuum scale.
Compute node configuration
LAMMPS was run for Rhodopsin benchmark. Rhodopsin benchmark simulates the movement of protein in the retina which in turn plays an important role in the perception of light. The protein is solvated lipid bilayer using the CHARMM force field with particle-particle particle-mesh long-range electrostatics and SHAKE constraints. The simulation was performed with 2,048,000 atoms at the temperature of 300K and pressure of 1 atm. The results for single node, two nodes and four nodes are as shown below. On one node with CPU only configuration, the loop-time was 66.5 seconds, while configuration of CPUs and two Intel Xeon Phi 7120Ps had a loop-time of 34.8 seconds. This demonstrated a performance increase of 1.9X. In comparison to CPUs only, CPUs + co-processors from one node to four nodes showed performance increase of 5.2X.
The LAMMPS power consumption analysis with RHODOPSIN benchmark is shown below. On single node, the power consumption by a CPU-only configuration was 442.4 watts, while configuration with CPUs and one co-processor consumed around 423W and subsequently configuration with CPUs and two co-processors consumed 450.8W.
All the LAMMPS runs on co-processors used the auto-balance mode. The performance per watt demonstrated 2 fold increase with CPUs + 2 co-processors than CPUs only.
The Intel Xeon Phi 7120Ps cluster with Dell PowerEdge R730 showed sustained performance increase of two fold. The power-efficiency was increased by 2X with two Intel Xeon Phi 7120Ps in comparison to CPUs only, resulting in a powerful, energy-efficient HPC platform.
One of the highlights of the Supercomputing Conference is always the Student Cluster Competition. It's an opportunity to see some of the future superstars of our industry demonstrate their skill under some pretty intense (but fun) pressure!
The student cluster competition also provides the competitors with a variety of opportunities. For some undergrads it is their first chance to focus exclusively on HPC. For other students the competition affords them the opportunity to make important networking and mentoring connections.
College teams from around the world are already gearing up for the Student Cluster Competition at SC 15 this November in Austin. The hometown favorites are no doubt feeling the pressure to fourpeat! It's an exciting time for everyone involved - especially the students.
Threepeat winners from SC14 in New Orleans.
You can learn more about the Student Cluster Competition and its positive impact on the lives of participants in this video.
Medical professionals and patient advocates agree genomics, as part of a personalized medical plan, can garner the best results for patients. However, there remain several challenges that prevent organizations from adopting the necessary technologies. Among those challenges:
However, these challenges can be met. Working with an experienced vendor services team can help navigate governmental regulations. They're also able to help with the integration, storage and analysis of large amounts of data. And by starting small and growing with your organization's needs, servers and storage can be added incrementally.
By recognizing that the perceived obstacles can be overcome, healthcare organizations can begin to harness the answers offered by genomics, leading to better patient outcomes.
You can read more about the challenges and solutions, as well as access a white paper on the topic, here.