• Is Open Source The AI Nirvana for Intel?

    April 20, 2018

    Intel has been making some interesting moves in the community space recently, including free licenses for its compiler suite for educators and open source contributors can now be had, as can rotating 90 day licenses for its full System Studio environment for anyone who takes the time to sign up.

    In the AI space, Intel recently announced that its nGraph code for managing AI graph APIs has also been opened to the community. After opening it up last month, Intel has been followed up on the initial work on MXNet with further improvements to TensorFlow.

    The Next Platform …

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  • Will Chapel Mark Next Great Awakening for Parallel Programmers?

    April 10, 2018

    Just because supercomputers are engineered to be far more powerful over time does not necessarily mean programmer productivity will follow the same curve. Added performance means more complexity, which for parallel programmers means working harder, especially when accelerators are thrown into the mix.

    It was with all of this in mind that DARPA’s High Productivity Computing Systems (HPCS) project rolled out in 2009 to support higher performance but more usable HPC systems. This is the era that spawned systems like Blue Waters, for instance, and various co-design efforts from IBM and Intel to make parallelism within broader reach to the …

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  • Deep Learning In R: Documentation Drives Algorithms

    April 9, 2018

    Hard to believe, but the R programming language has been with us since 1993.

    A quarter century has now passed since the authors Gentleman and Ihaka originally conceived the R platform as an implementation of the S programming language.

    Continuous global software development has taken the original concepts originally inspired by John Chambers’ Scheme in 1975 to now include parallel computing, bioinformatics, social science and more recently complex AI and deep learning methods. Layers have been built on top of layers and today’s R looks nothing like 1990’s R.

    So where are we at, especially with the emerging opportunities …

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  • An Inside Look at What Powers Microsoft’s Internal Systems for AI R&D

    March 28, 2018

    For those who might expect Microsoft to favor its own Windows-centric platforms and tools to power comprehensive infrastructure for serving AI compute and software services for internal R&D groups, plan on being surprised.

    While Microsoft does rely on some core windows features and certainly its Azure cloud services, much of its infrastructure is powered by a broad suite of open source tools. As Jim Jernigan, senior R&D systems engineer at Microsoft Research told us at the GPU Technology Conference (GTC18) this week, the highest volume of workloads running on the diverse research clusters Microsoft uses for AI development are running …

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  • A First Look at Summit Supercomputer Application Performance

    March 28, 2018 Dan Olds

    Big iron aficionados packed the room when ORNL’s Jack Wells gave the latest update on the upcoming 207 petaflops Summit supercomputer at the GPU Technology Conference (GTC18) this week.

    In just eight years, the folks at Oak Ridge have pushed the high performance bar from the 18.5 teraflops Phoenix system to the 27 petaflops Titan. That’s a 1000X + improvement in eight years.

    Summit will deliver 5X to 10X more performance than the existing Titan machine, but what is noteworthy is how Summit will do this. The system is set to have far fewer nodes (18,688 for Titan versus ~4,600 …

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  • Mounting Complexity Pushes New GPU Profiling Tools

    March 27, 2018

    The more things change, the more they remain the same — as do the two most critical issues for successful software execution. First, you remove the bugs, then you profile. And while debugging and profiling are not new, they are needed now more than ever, albeit in a modernized form.

    The first performance analysis tools were first found on early IBM platforms in the early 1970s.  These performance profiles were based on timer interrupts that recorded “status words” set at predetermined specific intervals in an attempt to detect “hot spots” inside running code.  

    Profiling is even more critical today, …

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  • The Future of Programming GPU Supercomputers

    March 26, 2018

    There are few people as visible in high performance computing programming circles as Michael Wolfe—and fewer still with level of experience. With 20 years working on PGI compilers and another 20 years before that working on languages and HPC compilers in industry, when he talks about the past, present and future of programming supercomputers, it is worthwhile to listen.

    In his early days at PGI (formerly known as The Portland Group) Wolfe focused on building out the company’s suite of Fortran, C, and C++ compilers for HPC, a role that changed after Nvidia Tesla GPUs came onto the scene and …

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  • There is No Such Thing as Easy AI — But We’re Getting Closer

    March 22, 2018

    The dark and mysterious art of artificial intelligence and machine learning is neither straightforward, or easy. AI systems have been termed “black boxes” for this reason for decades now. We desperately continue to present ever larger, more unwieldy datasets to increasingly sophisticated “mystery algorithms” in our attempts to rapidly infer and garner new knowledge.  

    How can we try to make all of this just a little easier?

    Hyperscalers with multi-million dollar analytics teams have access to vast, effectively unlimited compute and storage of all shapes and sizes. Huge teams of analysts, systems managers, resilience and reliability experts are standing up …

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  • Machine Learning for Auto-Tuning HPC Systems

    March 6, 2018

    On today’s episode of “The Interview” with The Next Platform we discuss the art and science of tuning high performance systems for maximum performance—something that has traditionally come at high time cost for performance engineering experts.

    While the role of performance engineer will not disappear anytime soon, machine learning is making tuning systems—everything from CPUs to application specific parameters—less of a burden. Despite the highly custom nature of systems and applications, reinforcement learning is allowing new leaps in time-saving tuning as software learns what works best for user applications and architectures, freeing up performance engineers to focus on the finer …

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  • OpenACC Developments: Past, Present, and Future

    March 2, 2018

    On today’s episode of “The Interview” with The Next Platform we talk with Doug Miles who runs the PGI compilers and tools team at Nvidia about the past, present, and future of OpenACC with an emphasis on what lies ahead in the next release.

    Over the last few years we have described momentum with OpenACC in a number of articles covering everything from what it means in the wake of new memory options to how it pushes on OpenMP to develop alongside. Today, however, we take a step back with an HPC code veteran for the bigger picture and …

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