Lustre Founder Spots Haskell on HPC Horizon

By Nicole Hemsoth

June 24, 2013

As a small 20-person company, Boulder-based Parallel Scientific flies just under the mainstream radar, but for those who have been in the HPC community for a number of years, its CEO and Chief Architect Peter Braam is a recognizable name.

Braam’s major contribution to high performance computing geared up in 1999 at Carnegie Mellon, where he worked on the file system architecture that spun into Lustre. Inspired by that progress, Braam kicked that project out as a business, beginning Cluster File Systems, which was acquired by Sun in 2007. A year later, Braam let the Sun set and embarked on a new adventure with Parallel Scientific.

The company has a rather interesting business model–instead of focusing on particular problem area (outside of the general purview of tailoring environments for large-scale HPC deployments) they are letting the breeze carry them. For instance, they have found themselves focusing on parallel Haskell in the last few years, even though Braam says that such development might just be the shell for something much larger or entirely different as they continue. 

The saying goes that in Haskell, the function is a first-class citizen–and this status might make it a solid fit for a range of high performance computing environments. Despite what Braam admits is a daunting learning curve, there is an open field of possibilities for Haskell to infiltrate HPC. As it stands, there is an active community around it and around 5000 open source and tools available. But the real value for high performance computing, he argues, lies in Haskell’s productivity and correctness–a worth that’s been validated in select industrial use cases.

Arguably, Google and Facebook have brought more attention to Haskell in recent years, but there are a number of other notable uses that highlight Braam’s confidence in the functional language. For instance, Chicago-based Allston Trading, a high frequency trading company, uses Haskell in their trading infrastructure. AT&T is using it in their Network Security group to automate internet abuse complaint processing. Bank of American is using it in their backend data transformation and loading system and Credit Suisse’s Global Modeling and Analytics Group has been using it since 2006 to improve modeler productivity and open access to those models across the organization.

Biotech giant Amgen also uses Haskell for math-heavy models and to “break developers out of their development rut by giving them a new way to think about software. According to the company’s David Balaban, “Our experience is that using functional programming reduces the critical conceptual distance between thought/algorithms design and code.” But the real value says Balaban is the level of correctness they’ve been able to achieve.

As Amgen’s Balaban says “we have been able to develop code quickly and verify–to an applied mathematician’s satisfaction–the correctness of Haskell code straightforwardly; we have yet to achieve this with more traditional mainstream languages.” 

And indeed, there are plenty of mainstream languages that would seem to fit the bill for mathematical models that don’t come with the hike. Braam says that while R might seem like the most practical language for users like Amgen and others noted above, there are opportunities for error in R that Haskell won’t allow. When correctness is key–as it is with all of the above use cases–the learning curve of Haskell is worth the price if total accuracy is inherent. In fact, he laughingly admits that their business could eventually turn to just building a language that is “safer” than R on the correctness front. 

When it comes to the uptake of a language like Haskell with its learning curve and relatively isolated set of non end user-based functionality, Braam says it’s a matter of time. He points to languages like Python, which took a decade or more to wind a path through large-scale environments, HPC and commercial alike. Still, he notes, “you’d be surprised how many companies have a ‘secret’ Haskell department–a group of people that are highly productive dedicated to solving a serious problem. This is especially true in the financial world–mostly because of the correctness element.”

And true to that point, in grazing the user community for Haskell, the trend seems to be that few are using Haskell as an end user tool. However, users want to be able to construct robust software infrastructure that lets users maintain creativity and take advantage of domain-specific languages, which he says are not as difficult as one might think to implement in Haskell. He points to the example of a Square Kilometer Array (SKA) research group, which wants to the right set of primitives built that will allow the hardware to change and run on different hybrid platforms–an area that his company can help with. 

The idea for finding a wider market for parallel Haskell spun from work Braam and colleagues did around an XSTACK proposal which sought programming environments for exascale computers. The team put an emphasis on automatic parallelization via compilers–a trick that Braam said plenty outside of the HPC community (Facebook, for example) has perfected. It just hasn’t caught on in HPC–although said the same Erlang and Haskell tricks that have worked at that scale can automate parallelism across a multitude of HPC systems. 

His team is also hard at work perfecting their “Awesome Haskell FPGA Compiler” whihc lets developers express hardware solutions in a high-level domain specific language in much the same way their work with SKA is allowing. The environment would allow software simulation and testing in an interactive environment to wick away the long development time that are the bane of FPGA design. The solution that can be kicked to the overall environment and ready to run in some unique data-intensive areas, like SKA.

Braam is a Haskell believer in the same way he believed in his pioneering work on Lustre. “People said you’re wasting your time,” he reminisced. While the use cases may be small, as big data and more complex models drive further into both research and enterprise settings, the appeal of a functional language that emphasizes correctness and productivity will reveal itself.

 

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!

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

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 cryptocurr Read more…

By Doug Black

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

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

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

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

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

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

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

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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

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 ne Read more…

By Tiffany Trader

Leading Solution Providers

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 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 w 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 Read more…

By Steve Campbell

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 Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, 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 networ Read more…

By Tiffany Trader

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 Read more…

By John Russell

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 advance Read more…

By Tiffany Trader

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