October 25, 2017

A new choice has appeared among the machine learning processors that are essential to AI technologies. We spoke with Takuya Araki from the System Platforms Laboratories about the new generation of vector processors which achieve stunning processing speed increases and cost reductions in fields where the computational GPUs popular in recent years had difficulty producing results.

New generation of processors that excel at parallel processing

Use Spark and Python without modifications

― Can they be linked to existing machine learning systems?

If you are a user of Spark (Note 1), which is typically used in machine learning today, then you can use them without modification. If you purchase and connect the products being announced now, we are developing middleware which will allow you to use them in the usual way from Spark, so you won't be aware of any hardware differences.
In addition, for Python (Note 2) we have also accommodated the scikit-learn (Note 3) interface, so you don't have to worry about any special settings or operations and can use it like always.

Eliminating waste and accelerating sparse matrix processing

― How has the processing efficiency of the vector calculation been improved?

The processing efficiency has been significantly improved due to two breakthroughs focusing on the characteristics of the "sparse matrix (Note 4)" which is often seen in large scale data handled by machine learning.
The first breakthrough is the "hybrid format" which divides the processing by the column and row to fully utilize the performance of the calculation unit and keeps the processing throughput at a high level. Indeed, the intrinsic level of performance is displayed by maintaining a high level of processing throughput in the vector calculation carrying out parallel processing all at once. However, wasted processing often occurred due to the sparse matrices with processing throughput deviations that are often handled in machine learning. In the newly developed technology, we figured out a way to divide the processing by the row and column to maintain a high level of processing throughput for each cycle.
While the processing within one processor accelerated due to the development of the hybrid format, another important factor is the communication for each processor. Because the arithmetically derived values are organized and then distributed to each processor to be calculated again in a process that is repeated many times until the learning converges, the communication speed is an extremely important factor in machine learning.
In the newly developed technology, we focused on the fact that there is no need to transmit columns without values in the sparse matrix for the corresponding result and only transmit the columns where values exist. As a result, we were able to reduce the communication volume for some data from 100 MB to 5 MB for one cycle.

Approaching supercomputer levels of performance

― Who are your target customers?

Vector computers are an architecture which was previously developed in the supercomputer field. However, as a result of this new technology, tower and rack mounted versions have also been added to the hardware product lineup. I believe that they can contribute to improving processing speed and reducing cost and space not only for customers who are already using NEC's SX-ACE vector supercomputer but also those customers who are currently building large scale clusters to engage in machine learning. For example, they can provide constructive proposals such as timely recommendations in a company running an EC site or optimized and efficient ad displays in a company handling web ads.
Furthermore, they are now available in an affordable price range for customers who were previously unable to implement vector computers due to the high cost. I believe that we have developed a solution that can be offered as a new alternative in high-speed processing computers, so customers should feel free to contact us.

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