arXiv subjects

Kiran Kumar Matam

Publications and source records attributed to Kiran Kumar Matam.

4 recordsLinked to original sources

arXiv2023

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments.

cs.DC↗

arXiv2021

First-Generation Inference Accelerator Deployment at Facebook

In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesses, large model sizes, as well as high compute, memory and network bandwidth requirements. We co-designed a high-performance, energy-efficient inference accelerator platform based on these requirements. We describe the inference accelerator platform ecosystem we developed and deployed at Facebook: both hardware, through Open Compute Platform (OCP), and software framework and tooling, through Pytorch/Caffe2/Glow. A characteristic of this ecosystem from the start is its openness to enable a variety of AI accelerators from different vendors. This platform, with six low-power accelerator cards alongside a single-socket host CPU, allows us to serve models of high complexity that cannot be easily or efficiently run on CPUs. We describe various performance optimizations, at both platform and accelerator level, which enables this platform to serve production traffic at Facebook. We also share deployment challenges, lessons learned during performance optimization, as well as provide guidance for future inference hardware co-design.

cs.AR↗

arXiv2021

Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models

Checkpoints play an important role in training long running machine learning (ML) models. Checkpoints take a snapshot of an ML model and store it in a non-volatile memory so that they can be used to recover from failures to ensure rapid training progress. In addition, they are used for online training to improve inference prediction accuracy with continuous learning. Given the large and ever increasing model sizes, checkpoint frequency is often bottlenecked by the storage write bandwidth and capacity. When checkpoints are maintained on remote storage, as is the case with many industrial settings, they are also bottlenecked by network bandwidth. We present Check-N-Run, a scalable checkpointing system for training large ML models at Facebook. While Check-N-Run is applicable to long running ML jobs, we focus on checkpointing recommendation models which are currently the largest ML models with Terabytes of model size. Check-N-Run uses two primary techniques to address the size and bandwidth challenges. First, it applies incremental checkpointing, which tracks and checkpoints the modified part of the model. Incremental checkpointing is particularly valuable in the context of recommendation models where only a fraction of the model (stored as embedding tables) is updated on each iteration. Second, Check-N-Run leverages quantization techniques to significantly reduce the checkpoint size, without degrading training accuracy. These techniques allow Check-N-Run to reduce the required write bandwidth by 6-17x and the required capacity by 2.5-8x on real-world models at Facebook, and thereby significantly improve checkpoint capabilities while reducing the total cost of ownership.

cs.IR↗

arXiv2020

PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs

Graph analytics are at the heart of a broad range of applications such as drug discovery, page ranking, and recommendation systems. When graph size exceeds memory size, out-of-core graph processing is needed. For the widely used external memory graph processing systems, accessing storage becomes the bottleneck. We make the observation that nearly all graph algorithms have a dynamically varying number of active vertices that must be processed in each iteration. However, existing graph processing frameworks, such as GraphChi, load the entire graph in each iteration even if a small fraction of the graph is active. This limitation is due to the structure of the data storage used by these systems. In this work, we propose to use a compressed sparse row (CSR) based graph storage that is more amenable for selectively loading only a few active vertices in each iteration. But CSR based systems suffers from random update propagation to many target vertices. To solve this challenge, we propose to use a multi-log update mechanism that logs updates separately, rather than directly update the active edges in a graph. Our proposed multi-log system maintains a separate log per each vertex interval. This separation enables us to efficiently process each vertex interval by just loading the corresponding log. Further, while accessing SSD pages with fewer active vertex data, we reduce the read amplification due to the page granular accesses in SSD by logging the active vertex data in the current iteration and efficiently reading the log in the next iteration. Over the current state of the art out-of-core graph processing framework, our PartitionedVC improves performance by up to $17.84\times$, $1.19\times$, $1.65\times$, $1.38\times$, $3.15\times$, and $6.00\times$ for the widely used bfs, pagerank, community detection, graph coloring, maximal independent set, and random-walk applications, respectively.

cs.DC↗