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!

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

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

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

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

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

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

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

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

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

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

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

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

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

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

Leading Solution Providers

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. 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

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

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

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. 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

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

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