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!

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale companies and their embrace of AI and deep learning – tha Read more…

By Doug Black

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “global” launch event in Austin TX. In many ways it was a fu Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it, analysts and journalists want to report on it. Deep learni Read more…

By Doug Black

OpenACC Shows Growing Strength at ISC

June 19, 2017

OpenACC is strutting its stuff at ISC this year touting expanding membership, a jump in downloads, favorable benchmarks across several architectures, new staff members, and new support by key HPC applications providers, Read more…

By John Russell

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major shakeups -- China still has the top two spots locked with th Read more…

By Tiffany Trader

ISC: Extreme-Scale Requirements to Push the Frontiers of Deep Learning

June 17, 2017

Deep learning is the latest and most compelling technology strategy to take aim at the decades-old “drowning in data/starving for insight” problem. But contrary to the commonly held notion, deep learning is more than Read more…

By Doug Black

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD, Cray, Hewlett Packard Enterprise (HPE), IBM, Intel, and NV Read more…

By John Russell

OpenSuCo: Advancing Open Source Supercomputing at ISC

June 15, 2017

As open source hardware gains traction, the potential for a completely open source supercomputing system becomes a compelling proposition, one that is being investigated by the International Workshop on Open Source Super Read more…

By Tiffany Trader

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

AMD Charges Back into the Datacenter and HPC Workflows with EPYC Processor

June 20, 2017

AMD is charging back into the enterprise datacenter and select HPC workflows with its new EPYC 7000 processor line, code-named Naples, announced today at a “g Read more…

By John Russell

Hyperion: Deep Learning, AI Helping Drive Healthy HPC Industry Growth

June 20, 2017

To be at the ISC conference in Frankfurt this week is to experience deep immersion in deep learning. Users want to learn about it, vendors want to talk about it Read more…

By Doug Black

OpenACC Shows Growing Strength at ISC

June 19, 2017

OpenACC is strutting its stuff at ISC this year touting expanding membership, a jump in downloads, favorable benchmarks across several architectures, new staff Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

ISC: Extreme-Scale Requirements to Push the Frontiers of Deep Learning

June 17, 2017

Deep learning is the latest and most compelling technology strategy to take aim at the decades-old “drowning in data/starving for insight” problem. But cont Read more…

By Doug Black

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

OpenSuCo: Advancing Open Source Supercomputing at ISC

June 15, 2017

As open source hardware gains traction, the potential for a completely open source supercomputing system becomes a compelling proposition, one that is being inv 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. Just how close real-wo 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 cam 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 a 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 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

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

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

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

Leading Solution Providers

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

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. 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 Read more…

By Alex Woodie

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

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

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

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

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