Microsoft Releases New Software Tools for Researchers

By Michael Feldman

July 13, 2009

As scientists increasingly rely on big data to drive their research, a new set of software tools is emerging. Two of these new tools, developed by Microsoft’s External Research division, were launched on Monday at the Microsoft Research Faculty Summit in Redmond, Wash. They include the Project Trident workbench and the Dryad/DryadLINQ programming environment.

Project Trident was originally aimed at oceanographic applications (hence the name). The work began as a collaboration between Microsoft External Research, the University of Washington and the Monterey Bay Aquarium to provide a high-level workflow tool for oceanographers. Oceanography, like many scientific domains today, is being inundated with a deluge of data that researchers are struggling to manage.

Once the proof-of-concept stage for Project Trident was completed, Microsoft realized it could be used as a general-purpose platform for other areas, such as astronomy, environmental science, medicine or essentially any type of research that is dominated by workflow issues. The data is coming from a growing number of inexpensive sensors that collect information in real time as well as an ever-expanding collection of scientific databases being stored on the Internet or in private repositories. In many cases, both data rates and data volumes are growing beyond the capabilities of traditional software environments.

Unlike the commercial world, the science community tends to freely pass its data around. But turning the raw information into useful knowledge often requires weeks, months or even years of software development involving customized scripts and applications. The whole idea behind Trident is to enable workflow applications to be developed by scientists, rather than programmers, by structuring the process into modular steps.

“Why lock your knowledge up into scripts or programs when you could actually write it in a tool that other people stand a chance of reusing,” asks Roger Barga, who is leading Microsoft’s development of Project Trident. According to him, researchers are recognizing that the model of customized workflow development is not sustainable. Even if software maintenance were less expensive, scientists are looking for the kind of speed and flexibility that a code rewrite does not allow.

The Trident workbench is being used today by oceanographers at the University of Washington for seafloor-based research that uses thousands of ocean sensors and by researchers at the Monterey Bay Aquarium Research Institute to study Typhoon intensification.  The workbench is also being employed by astronomers at Johns Hopkins University to support the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) project, which is looking for objects in the solar system that could pose a threat to Earth. In this case the data being ingested comes from an array of 1.4 gigapixels digital cameras that capture images of the night sky.

Ecogenomic sensors

In a nutshell, the Trident workbench tool provides a visual framework for managing and developing workflows. At startup, the user sees a library of existing workflows and activities (or workflow steps). In the GUI, one can add or delete steps from the pipeline by simply dragging and dropping. The idea is that domain experts with no programming knowledge can go in and mix and match existing workflow components to author new experiments and run them on the fly.

A typical workflow would start with reading in the raw data — data files and/or sensor devices. The next step would be to convert the various data sources into a common format. An analysis pipeline — filtering and conditioning algorithms — would come next. Typically the last step is to produce a visual representation of the result.

It’s not all just shuffling objects around a GUI, however. The individual activities, such as reading the raw data, analyzing it, and creating visualizations have to be developed in the first place, as you would any other piece of software. But once developed, the activities can be bound to any user-generated workflows. According to Barga, their experience has been that once you get more than a dozen or so workflows constructed, the users find they’re no longer writing much new code.

One of the important strengths of Trident is that it can utilize HPC clusters. Scientific analysis at scale often requires a high performance computing platform for reasonable performance. By default Trident assumes a single node execution, but users can schedule a job across multiple cluster nodes by creating a workflow application that communicates with the HPC job scheduler.

As one might have guessed, the assumed clustering environment here is Microsoft’s Windows HPC Server, but Trident does allow you to plug in your own scheduler too. This enables researchers to run on a Linux cluster, which remains a much more common platform today for high performance computing. Barga says plugging into a non-Windows scheduler is just one of the different ways Trident has been designed for extensibility, noting that even the tool’s GUI can be replaced should users wish to have a customized look and feel. One dependency that cannot be jettisoned, however, is the Windows .NET framework. The .NET environment contains the Windows Workflow, which is the foundation of the Trident workbench.

The other tools Microsoft released on Monday — Dryad and DryadLINQ — are aimed at developers rather than end users. Dryad itself is a general-purpose data parallel programming runtime designed to run distributed applications on Windows clusters. The runtime is responsible for scheduling resources, handling hardware and software failures, and distributing data and code across the cluster as needed. DryadLINQ is an abstraction layer that runs LINQ (Language Integrated Query) operations on top of Dryad, the idea being to be able to execute data queries that automatically get parallelized via the Dryad runtime.

Unlike MPI, Dryad is not for latency sensitive computation. It is aimed at applications that can increase data throughput via loosely-coupled parallelization. Microsoft Research itself uses Dryad internally for search engine and machine learning research. Barga says they have scaled such applications up to 3,000 nodes on a Windows HPC Server cluster, noting that some of these jobs run for dozens of hours. “The beauty of the Dryad runtime is that if an individual node drops out or there’s a failure in one of the jobs, Dryad automatically recovers, moving the computation off the failed node and reproducing inputs that node was responsible for,” says Barga.

Microsoft is really offering Trident and Dryad/DryadLINQ as two separate solutions, but with interoperability. Trident includes a pre-defined custom activity that invokes Dryad/DryadLINQ, allowing the programmer to pass it LINQ queries. But the real intention seems to be to encourage users to develop their own Dryad/DryadLINQ components to hook into the Trident workbench or use them in standalone applications.

Trident and Dryad/DryadLINQ will be released under the MSR-LA license (Microsoft Research License Agreement) and, as such, is for non-commercial academic use only. Barga says Microsoft is considering some sort of license arrangement for commercial users, but without any requirement for royalty paybacks. The bottom line here is that Microsoft is not looking to generate revenue directly from these tools, but rather to expand the Windows ecosystem for researchers and encourage use of the Windows HPC Server platform.

Barga couldn’t talk about any future interoperability between these tools and Microsoft’s Azure cloud computing platform, but it’s reasonable to assume that all these technologies are heading toward convergence. “Science is moving to the cloud and we want to make sure that all of the tools that we offer, including things like Dryad and Trident … will work on the cloud for scientists who want to do really big data challenges,” says Barga.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This