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."

"Hardware acceleration for applications delivers efficiency and flexibility into the AI space, including subsets such as machine learning," Krish Prasad, …

Today Mellanox announced that its RDMA (Remote Direct Memory Access) networking solutions for VMware vSphere enable virtualized Machine Learning solutions that achieve higher GPU utilization and efficiency. The benchmark was performed on a four-node cluster running vSphere 6.7 equipped with NVIDIA T4 GPUs with vCS software and Mellanox ConnectX-5 100 GbE SmartNICs, all connected by a Mellanox Spectrum SN2700 100 GbE switch. The PVRDMA Ethernet solution enables VM-to-VM communication over RDMA, which boosts data communication performance in virtualized environments while achieving significantly higher efficiency compared with legacy TCP/IP transports. Additionally, PVRDMA retains core virtual machine capabilities such as vMotion. This translates to real-world customer advantages including optimized server and GPU utilization, reduced machine learning training time and improved scalability.