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Intel working with Alibaba on cloud

by on10 March 2017


Cloud-based FPGA

Alibaba Cloud (Aliyun), has announced a pilot program with Chipzilla for a cloud-based FPGA (field programmable gate array) acceleration service with the goal of enabling customers to have virtual access to powerful compute resources in the cloud to help them manage business, scientific and enterprise data application workloads more effectively.

By using Intel Arria 10 FPGAs, Intel Xeon processor-based servers and software development tools for application acceleration as a ready-to-go preconfigured infrastructure, Alibaba Cloud offers systems designers cloud-based workload acceleration as an alternative to investing in on-premises FPGA infrastructure.

The service delivers on-demand scalability of workload acceleration with FPGAs while reducing upfront investment risks and accelerating delivery of new infrastructure services.

Senior director, Alibaba Cloud the appropriately named Jin Li said that Alibaba Cloud offers customers access to a number of services in the cloud, and adding an FPGA-based acceleration offering means they can access it without the cost or requirement of building out their own infrastructure.

"This service greatly adds to our value as a leading provider of highly scalable cloud computing and data management services that provide businesses with flexible, reliable connectivity," Jin said.

One of the key benefits of FPGAs is that they are programmable and can be customized to accelerate and scale for varying workloads, such as machine learning, data encryption and media transcode.

Dan McNamara, corporate vice president and general manager, Intel Programmable Solutions Group said that Intel FPGAs were enabling new business models such as Alibaba's approach of using FPGAs to accelerate diverse workloads via cloud services.

"In addition, Intel offers customers scalable solutions for accelerated computing with its data center leadership in Intel Xeon processors, FPGAs, optimized tools and software, and a global partner ecosystem across the spectrum of deployment models."


Last modified on 10 March 2017
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