Kiran Kumar Matam
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View article: High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models.
High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. Open
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-…
View article: Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models Open
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-…
View article: Check-N-Run: A Checkpointing System for Training Recommendation Models.
Check-N-Run: A Checkpointing System for Training Recommendation Models. Open
Checkpoints play an important role in training recommendation systems at scale. They are important for many use cases, including failure recovery to ensure rapid training progress, and online training to improve inference prediction accura…
View article: Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models
Check-N-Run: A Checkpointing System for Training Deep Learning Recommendation Models Open
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 tr…
View article: GraphSSD
GraphSSD Open
Graph analytics play a key role in a number of applications such as social networks, drug discovery, and recommendation systems. Given the large size of graphs that may exceed the capacity of the main memory, application performance is bou…
View article: PartitionedVC: Partitioned External Memory Graph Analytics Framework for\n SSDs
PartitionedVC: Partitioned External Memory Graph Analytics Framework for\n SSDs Open
Graph analytics are at the heart of a broad range of applications such as\ndrug discovery, page ranking, and recommendation systems. When graph size\nexceeds memory size, out-of-core graph processing is needed. For the widely\nused externa…
View article: PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs
PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs Open
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 m…
View article: Summarizer
Summarizer Open
Modern data center solid state drives (SSDs) integrate multiple general-purpose embedded cores to manage flash translation layer, garbage collection, wear-leveling, and etc., to improve the performance and the reliability of SSDs. As the p…