![]() With the recent release of CUDA 10 and 11, GPUs even gained the ability to execute complete computational graphs with complex control flows. Demand paging from host memory (NVIDIA Pascal in 2014 and AMD HSA in 2013), the ability to interact directly with host memory with unified addressing (UVA), and unified virtual memory (UVM) allowed the GPUs to execute with little to no CPU intervention. Over the past decade, GPUs have morphed from secondary offload devices to devices that can operate with increased autonomy. With Bitfusion, we have seen the number of users per GPU increase resulting in large infrastructure savings.įigure 1: vSphere Bitfusion: GPU pooling and partitioning CUDA TrendsĪs the NVIDIA CUDA stack has evolved over time, it has become more complete and more performant … but also more proprietary, closed, and coupled. ![]() This functionality allows a shared pool of GPUs to be used across users, use cases, time scales, and time zones. In 2020, we responded to these issues with the introduction of vSphere Bitfusion (acquired by VMware in 2019), which allows for GPU pooling and sharing over a network. Bitfusion: Changing the infrastructure equationĪt VMware, we recognized the problems mentioned above. GPUs did not make sense without a change in this infrastructure equation. Them, and overall GPU utilization rates began to decrease. Of teraflops per server grew, end users were unable to fully utilize and share Scale-out solutions emerged, such as the DGX POD, in 2019. In fact, CUDA continues to be the dominant API upon which many AI apps are built today.Ģ016, GPU servers were being built with increased density, such as the NVIDIAĭGX-1. RADIUM CHARGE SOFTWARENVIDIA’s CUDA ecosystem quickly improved and became one of the most complete and robust software stacks available. ![]() But the landscape beyond CPUs and GPUs was an unfamiliar one - a befuddling jumble of hardware and graph compilers requiring performance engineering. ![]() Furthermore, in their quest for speed, more companies began to develop specialized accelerators, such as AWS Inferentia, Google’s Cloud TPU, and Intel Habana. End users faced unstable software stacks, buggy drivers, and uneven performance. The emergence of GPUs as the preeminent AI/ML accelerators was fraught with growing pains, starting in about 2015. ![]()
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