Storage system suppliers showcase modern options to maintain tempo with quicker accelerators.
MLCommons® introduced outcomes for its industry-standard MLPerf® Storage v1.0 benchmark suite, which is designed to measure the efficiency of storage methods for machine studying (ML) workloads in an architecture-neutral, consultant, and reproducible method. The outcomes present that as accelerator expertise has superior and datasets proceed to extend in measurement, ML system suppliers should make sure that their storage options sustain with the compute wants. This can be a time of speedy change in ML methods, the place progress in a single expertise space drives new calls for in different areas. Excessive-performance AI coaching now requires storage methods which are each large-scale and high-speed, lest entry to saved knowledge turns into the bottleneck in your entire system. With the v1.0 launch of MLPerf Storage benchmark outcomes, it’s clear that storage system suppliers are innovating to satisfy that problem.
Model 1.0 storage benchmark breaks new floor
The MLPerf Storage benchmark is the primary and solely open, clear benchmark to measure storage efficiency in a various set of ML coaching eventualities. It emulates the storage calls for throughout a number of eventualities and system configurations masking a spread of accelerators, fashions, and workloads. By simulating the accelerators’ “suppose time” the benchmark can generate correct storage patterns with out the necessity to run the precise coaching, making it extra accessible to all. The benchmark focuses the take a look at on a given storage system’s potential to maintain tempo, because it requires the simulated accelerators to take care of a required degree of utilization.
Three fashions are included within the benchmark to make sure various patterns of AI coaching are examined: 3D-UNet, Resnet50, and CosmoFlow. These workloads provide a wide range of pattern sizes, starting from a whole lot of megabytes to a whole lot of kilobytes, in addition to wide-ranging simulated “suppose occasions” from just a few milliseconds to a couple hundred milliseconds.
The benchmark emulates NVIDIA A100 and H100 fashions as representatives of the presently obtainable accelerator applied sciences. The H100 accelerator reduces the per-batch computation time for the 3D-UNet workload by 76% in comparison with the sooner V100 accelerator within the v0.5 spherical, turning what was sometimes a bandwidth-sensitive workload into far more of a latency-sensitive workload.
As well as, MLPerf Storage v1.0 contains help for distributed coaching. Distributed coaching is a vital state of affairs for the benchmark as a result of it represents a typical real-world follow for quicker coaching of fashions with giant datasets, and it presents particular challenges for a storage system not solely in delivering greater throughput but in addition in serving a number of coaching nodes concurrently.
V1.0 benchmark outcomes present efficiency enchancment in storage expertise for ML methods
The broad scope of workloads submitted to the benchmark mirror the wide selection and variety of various storage methods and architectures. That is testomony to how vital ML workloads are to all kinds of storage options, and demonstrates the lively innovation taking place on this house.
“The MLPerf Storage v1.0 outcomes exhibit a renewal in storage expertise design,” stated Oana Balmau, MLPerf Storage working group co-chair. “In the mean time, there doesn’t look like a consensus ‘better of breed’ technical structure for storage in ML methods: the submissions we obtained for the v1.0 benchmark took a variety of distinctive and inventive approaches to offering high-speed, high-scale storage.”
The ends in the distributed coaching state of affairs present the fragile stability wanted between the variety of hosts, the variety of simulated accelerators per host, and the storage system as a way to serve all accelerators on the required utilization. Including extra nodes and accelerators to serve ever-larger coaching datasets will increase the throughput calls for. Distributed coaching provides one other twist, as a result of traditionally completely different applied sciences – with completely different throughputs and latencies – have been used for shifting knowledge inside a node and between nodes. The utmost variety of accelerators a single node can help will not be restricted by the node’s personal {hardware} however as a substitute by the power to maneuver sufficient knowledge shortly to that node in a distributed surroundings (as much as 2.7 GiB/s per emulated accelerator). Storage system architects now have few design tradeoffs obtainable to them: the methods should be high-throughput and low-latency, to maintain a large-scale AI coaching system operating at peak load.
“As we anticipated, the brand new, quicker accelerator {hardware} considerably raised the bar for storage, making it clear that storage entry efficiency has grow to be a gating issue for total coaching pace,” stated Curtis Anderson, MLPerf Storage working group co-chair. “To stop costly accelerators from sitting idle, system architects are shifting to the quickest storage they will procure – and storage suppliers are innovating in response.”
MLPerf Storage v1.0
The MLPerf Storage benchmark was created by means of a collaborative engineering course of throughout greater than a dozen main storage resolution suppliers and tutorial analysis teams. The open-source and peer-reviewed benchmark suite presents a degree taking part in area for competitors that drives innovation, efficiency, and vitality effectivity for your entire {industry}. It additionally offers important technical info for purchasers who’re procuring and tuning AI coaching methods.
The v1.0 benchmark outcomes, from a broad set of expertise suppliers, exhibit the {industry}’s recognition of the significance of high-performance storage options. MLPerf Storage v1.0 contains over 100 efficiency outcomes from 13 submitting organizations: DDN, Hammerspace, Hewlett Packard Enterprise, Huawei, IEIT SYSTEMS, Juicedata, Lightbits Labs, MangoBoost, Nutanix, Simplyblock, Volumez, WEKA, and YanRong Tech.
“We’re excited to see so many storage suppliers, each giant and small, take part within the first-of-its-kind v1.0 Storage benchmark,” stated David Kanter, Head of MLPerf at MLCommons. “It exhibits each that the {industry} is recognizing the necessity to maintain innovating in storage applied sciences to maintain tempo with the remainder of the AI expertise stack, and likewise that the power to measure the efficiency of these applied sciences is important to the profitable deployment of ML coaching methods. As a trusted supplier of open, truthful, and clear benchmarks, MLCommons ensures that expertise suppliers know the efficiency goal they should meet, and customers can procure and tune ML methods to maximise their utilization – and finally their return on funding.”
View the Outcomes
To view the outcomes for MLPerf Storage v1.0, please go to the Storage benchmark outcomes.
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