• Crunching Machine Learning And Databases Together On GPUs

    May 8, 2017

    While it is always best to have the right tool for the job, it is better still if a tool can be used by multiple jobs and therefore have its utilization be higher than it might otherwise be. This is one of the reasons why general purpose, X86-based computing took over the datacenter. Economies of scale trumped the efficiency that can come from limited scope or just leaving legacy applications alone in place on alternate platforms.

    The idea of offloading computational tasks from CPUs to GPU accelerators took off in academia a little more than a decade ago, and …

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  • Pushing A Trillion Row Database With GPU Acceleration

    April 26, 2017

    There is an arms race in the nascent market for GPU-accelerated databases, and the winner will be the one that can scale to the largest datasets while also providing the most compatibility with industry-standard SQL.

    MapD and Kinetica are the leaders in this market, but BlazingDB, Blazegraph, and PG-Strom also in the field, and we think it won’t be long before the commercial relational database makers start adding GPU acceleration to their products, much as they have followed SAP HANA with in-memory processing.

    MapD is newer than Kinetica, and it up until now, it has been content to allow clustering …

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  • Rapid GPU Evolution at Chinese Web Giant Tencent

    March 24, 2017

    Like other major hyperscale web companies, China’s Tencent, which operates a massive network of ad, social, business, and media platforms, is increasingly reliant on two trends to keep pace.

    The first is not surprising—efficient, scalable cloud computing to serve internal and user demand. The second is more recent and includes a wide breadth of deep learning applications, including the company’s own internally developed Mariana platform, which powers many user-facing services.

    When the company introduced its deep learning platform back in 2014 (at a time when companies like Baidu, Google, and others were expanding their GPU counts for speech and …

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  • Open Hardware Pushes GPU Computing Envelope

    March 17, 2017

    The hyperscalers of the world are increasingly dependent on machine learning algorithms for providing a significant part of the user experience and operations of their massive applications, so it is not much of a surprise that they are also pushing the envelope on machine learning frameworks and systems that are used to deploy those frameworks. Facebook and Microsoft were showing off their latest hybrid CPU-GPU designs at the Open Compute Summit, and they provide some insight into how to best leverage Nvidia’s latest “Pascal” Tesla accelerators.

    Not coincidentally, the specialized systems that have been created for supporting machine learning workloads …

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  • 3D Stacking Could Boost GPU Machine Learning

    March 14, 2017

    Nvidia has staked its growth in the datacenter on machine learning. Over the past few years, the company has rolled out features in its GPUs aimed neural networks and related processing, notably with the “Pascal” generation GPUs with features explicitly designed for the space, such as 16-bit half precision math.

    The company is preparing its upcoming “Volta” GPU architecture, which promises to offer significant gains in capabilities. More details on the Volta chip are expected at Nvidia’s annual conference in May. CEO Jen-Hsun Huang late last year spoke to The Next Platform about what he called the upcoming “hyper-Moore’s Law” …

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  • For Big Banks, Regulation is the Mother of GPU Invention

    March 2, 2017

    There is something to be said for being at the right place at the right time.

    While there were plenty of folks who were in the exact wrong spot when the financial crisis hit in 2007-2008, some technologies were uniquely well timed to meet the unexpected demands of a new era.

    In the aftermath of the crash, major investment banks and financial institutions had a tough task ahead to keep up with the wave of regulations instituted to keep them straight. This has some serious procedural impacts, and also came with some heady new demands on compute infrastructure. Post-regulation, investment …

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  • Nvidia Tesla Compute Business Quadruples In Q4

    February 13, 2017

    If Nvidia’s Datacenter business unit was a startup and separate from the company, we would all be talking about the long investment it has made in GPU-based computing and how the company has moved from the blade of the hockey stick and rounded the bend and is moving rapidly up the handle with triple-digit revenue growth and an initial public offering on the horizon.

    But the part of Nvidia’s business that is driven by its Tesla compute engines and GRID visualization engines is not a separate company and it is not going public. Still, that business is sure making things …

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  • Pushing MPI into the Deep Learning Training Stack

    February 7, 2017

    We have written much about large-scale deep learning implementations over the last couple of years, but one question that is being posed with increasing frequency is how these workloads (training in particular) will scale to many nodes. While different companies, including Baidu and others, have managed to get their deep learning training clusters to scale across many GPU-laden nodes, for the non-hyperscale companies with their own development teams, this scalability is a sticking point.

    The answer to deep learning framework scalability can be found in the world of supercomputing. For the many nodes required for large-scale jobs, the de facto …

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  • Why Google Is Driving Compute Diversity

    January 10, 2017

    In the ideal hyperscaler and cloud world, there would be one processor type with one server configuration and it would run any workload that could be thrown at it. Earth is not an ideal world, though, and it takes different machines to run different kinds of workloads.

    In fact, if Google is any measure – and we believe that it is – then the number of different types of compute that needs to be deployed in the datacenter to run an increasingly diverse application stack is growing, not shrinking. It is the end of the General Purpose Era, which began …

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  • Nvidia CEO’s “Hyper-Moore’s Law” Vision for Future Supercomputers

    November 28, 2016

    Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. This next year promises far more on both horizons and while GPU maker Nvidia might not have seen it coming to this extent when it was outfitting its first GPUs on the former top “Titan” supercomputer, the company sensed a mesh on the horizon when the first hyperscale deep learning shops were deploying CUDA and GPUs to train neural networks.

    All of this portends an exciting year ahead and for once, the mighty CPU …

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