Today we talked with VAST Data’s Subramanian Kartik (@phyzzycyst), Global Systems Engineering Lead and Howard Marks (@DeepStorage@mastodon.social, @deepstoragenet) former GreyBeards co-host and now Technologist Extraordinary & Plenipotentiary at VAST. Howard needs no introduction to our listeners but Kartik does. Kartik has supported a number of customers implementing AI apps at VAST and prior companies, so he is well versed in the reality of AI ML DL. Moreover, VAST recently funded Silverton Consulting to write a paper discussing Deep Learning IO.
Although AI ML DL applications have been very popular these days in IT, there’s been a continuing challenge trying to understand its IO requirements. Listen to the podcast to learn more.
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AI ML DL Neural Networks (NN) models train with data and lots of it while inferencing is also very data dependent. Kartik said AI model IO consists of small block, random reads with very few writes.
Some models contain huge NNs which consume mountains of data to train while others are relatively small and consume much less. GPT-3(.5), the model behind the original ChatGPT, has ~75B parameters in its ~800GB NN.
As many of us know, the key to AI processing is GPU hardware, which performs most, if not all, of the computations to train models and supply inferences. Moreover, to maximize training throughput, many organizations deploy model parallelism, using 10s to 1000s of GPUs.
For instance, in the paper mentioned earlier, we showed a model training IO chart based on all six storage vendor published NVIDIA DGX-A100 Reference Architecture reports for ResNet-50. On this single chart, all 6 storage systems supplied roughly the same images processed/sec (or ~IO bandwidth) performance to train the model on each of 8, 16 & 32 GPUs configurations. This is very unusual from our perspective but shows that ResNet-50 training is not IO bound.
However, another approach to speeding up NN training is to take advantage of newer, more advanced IO protocols. NVIDIA GPUDirect Storage transfers data directly from storage memory to GPU memory bypassing CPU memory all together which can significantly speed up GPU data consumption. It turns out that one bottleneck for AI training is CPU memory bandwidth
In addition, most AI model training reads data from a single file system mount point. Historically, an NFS mount point was limited to a single TCP connection and a maximum of ~2.5GB/sec of IO bandwidth. Recently, however, NConnect for NFS has been introduced which increased TCP connections to 16 per mount point .
Despite that, VAST Data found that by adding some code to Linux’s NFS TCP stack, they were able to increase NConnect to 64 TCP connections per compute node. Howard mentioned that with these changes and a 16 (compute) node VAST Data storage cluster they sustained 175GB/sec of GPUDirect Storage bandwidth using a DGX-A100 systems .
Subramanian Kartik, Global Systems Engineering Lead, VAST Data
Subramanian Kartik has been the Vice President of Systems Engineering at VAST Data since January of 2020, running the global presales organization. He is part of the incredible success of VAST Data which increased almost 10-fold in valuation and revenue in this period.
An accomplished technologist and executive in the industry, he has a wide array of experience in Cloud Architectures, AI/Machine Learning/Deep Learning, as well as in the Life Sciences, covering high-performance computing and storage. He has had a lifelong deep passion for studying complex problems in all spheres spanning both workloads and infrastructure at the vanguard of current day technology.
Prior to his work at VAST Data, he was with EMC (later Dell) for two decades, as both a Distinguished Engineer and global executive running the Converged and Hyperconverged Division go-to-market. He has a Ph.D in Particle Physics with over 75 publications and 3 patents to his credit over the years. He enjoys mathematics, jazz, cooking and travelling with his family in his non-existent spare time.
Howard Marks, (former GreyBeards Co-Host) Technologist Extraordinary and Plenipotentiary, VAST Data
Howard Marks brings over forty years of experience as a technology architect for hire and Industry observer to his role as VAST Data’s Technologist Extraordinary and Plienopotentary. In this role, Howard demystifies VAST’s technologies for customers and customer requirements for VAST’s engineers.
Before joining VAST, Howard ran DeepStorage an industry test lab and analyst firm. An award-winning speaker, he has appeared at events on three continents including Comdex, Interop and VMworld.
Howard is the author of several books (all gratefully out of print) and hundreds of articles since Bill Machrone taught him journalism at PC Magazine in the 1980s.
Listeners may also remember that Howard was a founding co-Host of the Greybeards-on-Storage Podcast.