NEC to accelerate machine learning for vector PCsPosted On 2017-07-17
NEC Corporation has reported that it has created data processing technology that quickens the execution of machine learning on vector computers by more than 50 times in contrast with Spark Technologies.
This recently created data processing uses computing and communication innovations that use "sparse matrix” data structures keeping in mind the ultimate goal to fundamentally accelerate the execution of vector PCs in machine learning.
Furthermore, NEC developed middleware that integrates sparse matrix structures in order to abridge the use of machine learning. In this, users are able to effortlessly launch this middleware from Python or Spark infrastructures, which are commonly used for data analysis, without special programming.
General Manager, Yuichi Nakamura, System Platform Research Laboratories, NEC Corporation said, "This technology permits users to quickly benefit from the results of machine learning, including the optimized placement of web advertisements, recommendations, and document analysis." He furthermore added, “low-cost analysis using a small number of servers enables a wide range of users to take advantage of large-scale data analysis that was formerly only available to large companies.”
NEC's next-generation vector PC (*2) is being developed to adaptably meets an extensive variety of cost and execution needs. This data processing technology extends the abilities of next-generation vector PCs to incorporate huge scale data analysis, for instance, machine learning, notwithstanding numerical computation, the conventional specialty of vector PCs.
NEC will present this innovation on July 5 at the International Symposium on Parallel and Distributed Computing 2017 (ISPDC-2017) held in Innsbruck, Austria, from Monday, July 3 to Thursday, July 6.