Case Study: Speeding Up Machine Learning by 1,000 Fold

December 11th, 2012 by Ken Elkabany

One of our most exciting partnerships has been with D-Wave Systems. Their technical team, led by Founder and CTO Geordie Rose, has an incredibly bold vision for the future. One that has led them to build the world’s first large-scale adiabatic quantum computer, the D-Wave One.

To understand how PiCloud is used with a quantum computer, we can draw a parallel to the relationship between a CPU and GPU. A CPU is general purpose, and is responsible for running an application and controlling its flow. It only calls on a GPU for specialized tasks, particularly SIMD-favorable operations commonly found in graphics. Similar to a GPU, the D-Wave One requires a general-purpose computing cluster to work alongside it, as it only solves highly-specialized problems. Rather than building their own compute cluster to complement their quantum computer, D-Wave turned to us. Geordie writes:

“We wouldn’t have been able to do the project at all, as none of us had the experience necessary to build the infrastructure PiCloud provides.

The D-Wave One, and its planned successors, are designed to find solutions to the Ising model, which has broad applications in machine learning including in deep learning. For those interested in the optimization model, D-Wave’s software running on PiCloud is responsible for generating feature vectors, while the D-Wave One is responsible for generating weight vectors for fitting those features to represent some data array (image, audio, text, …). The process is iterative where each iteration optimizes either weights or features, while the other is held constant.

The results have been a success. Geordie continues:

“We have achieved speedups on the order of 1,000 times faster for large
unsupervised feature learning jobs bringing tasks that would have taken six months on single workstations down to less than half a day.”

Geordie has identified the two underlying reasons for PiCloud popularity in computationally-heavy disciplines. The first is the access we provide to an unparalleled amount of computing power. The second is the ability to use said computing power without the need for in-house expertise.

You can download the full D-Wave case study, or view it in your browser below.

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Categories: Case Study, Success Story

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One Response to “Case Study: Speeding Up Machine Learning by 1,000 Fold”

  1. Bo says:

    Feature request: Interface one of DWAVE’s machines with PiCloud’s grid, so that PiCloud’s academic clients can make use of the quantum machine.

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