It is common knowledge in the manufacturing sector of the economy that many of the companies that should have deployed HPC simulation and modeling applications one or two decades ago to help with product design, among other tasks, did not do so. With the lowering of costs of HPC clusters based on X86 processors and the advent of open source software to perform many tasks, this seemed counterintuitive. And very annoying to those who were trying to address "the missing middle," as it came to be called. There are a lot of theories about what went wrong here, but at the very least, even with the lowering of the cost of HPC wares, buying a cluster, managing it, and keeping it busy to therefore justify the substantial investment was tough to rationalize, no matter the potential returns. Then there was the difficulty of sorting out how to make an HPC workflow, consisting of multiple applications hooked together, work well when existing systems – designing with scientific workstations and easier to use CAD/CAM tools – did the job.

Update: As of August 2019, the Bitfusion acquisition has closed. We would like to welcome Bitfusion and its staff to VMware. Increasingly businesses are applying artificial intelligence (AI) technologies to differentiate and advance their processes and offerings. Today, I am pleased to announce our intent to acquire Bitfusion to help businesses more efficiently use AI technologies on-premises and in hybrid cloud environments. Hardware acceleration for applications--which can take the form of GPUs (graphics processor units), FPGAs (field-programmable gate arrays), and ASICs (application-specific integrated circuits)--delivers efficiency and flexibility into the AI space including subsets such as machine learning.

In July, VMware acquired Bitfusion, a company whose technology virtualizes compute accelerators with the goal of enabling modern workloads like artificial intelligence and data analytics to take full advantage of systems with GPUs or with FPGAs. Specifically, Bitfusion's software allows for virtual machines to offload compute duties to GPUs, FPGAs, or even other kinds of ASICs. The deal didn't get a ton of attention at the time, but for VMware, it was an important step in realizing its cloud ambitions. "Hardware acceleration for applications delivers efficiency and flexibility into the AI space, including subsets such as machine learning," Krish Prasad, senior vice president and general manager of VMware's Cloud Platform business unit, wrote in a blog post announcing the acquisition. "Unfortunately, hardware accelerators today are deployed with bare-metal practices, which force poor utilization, poor efficiencies, and limit organizations from sharing, abstracting and automating the infrastructure. This provides a perfect opportunity to virtualize them – providing increased sharing of resources and lowering costs."

Please join us at VMworld for our Elastic AI talk on Monday, Aug 27, from 2:30 p.m. – 3:30 p.m. to learn how we can help you drive increased efficiencies in your enterprise AI deployments. Bitfusion FlexDirect can connect any virtual machine (VM), running any AI framework, to any GPU or partial GPUs, hosted by ESXi or bare-metal. These VMs can schedule AI/ML workloads and launch them on any compute server, regardless of whether there are local GPUs attached to the server. FlexDirect supports every CUDA enabled GPU as well as all GPU servers, with any density of GPUs. We are now able to support a mixed environment of ESXi compute servers, connected to GPU farms which are bare-metal or part of vSphere.

Deep learning users can now access pre-configured NVIDIA DIGITS Titan-X GPU instance starting at 49 cents per hour on Nimbix! Data Scientists and Deep learning users can now try the single-click solution on Cloud Service Provider Nimbix to launch an instance configured to run NVIDIA DIGITS on Titan-X GPUs at as low as 0.49/hour, the most affordable high performance GPUs compared to anywhere in the cloud, powered by Bitfusion Boost's Software Defined Supercompute technology. The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers. Best of all, DIGITS is a complete system so you don't have to write any code. You can train neural network models, including pre-built AlexNet and GoogleNet models.