222 citations · 397 across the 21 of their papers we have counts for
21 papers
Eiger: An Efficient Library for GPU-based Data Analytics
Bowen Wu, Marko Kabić, Sven Hepkema +3
GPUs have become an increasingly attractive platform for accelerating analytical workloads due to their massive parallelism and high memory bandwidth. Recent studies show that in s…
Zelda: Video Analytics using Vision-Language Models
Francisco Romero, Caleb Winston, Johann Hauswald +2
Advances in ML have motivated the design of video analytics systems that allow for structured queries over video datasets. However, existing systems limit query expressivity, requi…
FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models
Geet Sethi, Pallab Bhattacharya, Dhruv Choudhary +2
Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing us…
Transactions Make Debugging Easy
Qian Li, Peter Kraft, Michael Cafarella +6
We propose TROD, a novel transaction-oriented framework for debugging modern distributed web applications and online services. Our critical insight is that if applications store al…
RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure
Mark Zhao, Dhruv Choudhary, Devashish Tyagi +9
We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addr…
Apiary: A DBMS-Integrated Transactional Function-as-a-Service Framework
Peter Kraft, Qian Li, Kostis Kaffes +16
Developers increasingly use function-as-a-service (FaaS) platforms for data-centric applications that perform low-latency and transactional operations on data, such as for microser…
SOL: Safe On-Node Learning in Cloud Platforms
Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar +3
Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfor…
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
Geet Sethi, Bilge Acun, Niket Agarwal +3
We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two ke…
Practical Scheduling for Real-World Serverless Computing
Kostis Kaffes, Neeraja J. Yadwadkar, Christos Kozyrakis
Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has bee…
Faa$T: A Transparent Auto-Scaling Cache for Serverless Applications
Francisco Romero, Gohar Irfan Chaudhry, Íñigo Goiri +6
Function-as-a-Service (FaaS) has become an increasingly popular way for users to deploy their applications without the burden of managing the underlying infrastructure. However, ex…
ShEF: Shielded Enclaves for Cloud FPGAs
Mark Zhao, Mingyu Gao, Christos Kozyrakis
FPGAs are now used in public clouds to accelerate a wide range of applications, including many that operate on sensitive data such as financial and medical records. We present ShEF…
Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines
Francisco Romero, Mark Zhao, Neeraja J. Yadwadkar +1
The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, repre…
RackSched: A Microsecond-Scale Scheduler for Rack-Scale Computers (Technical Report)
Hang Zhu, Kostis Kaffes, Zixu Chen +4
Low-latency online services have strict Service Level Objectives (SLOs) that require datacenter systems to support high throughput at microsecond-scale tail latency. Dataplane oper…
DBOS: A Proposal for a Data-Centric Operating System
Michael Cafarella, David DeWitt, Vijay Gadepally +5
Current operating systems are complex systems that were designed before today's computing environments. This makes it difficult for them to meet the scalability, heterogeneity, ava…
INFaaS: A Model-less and Managed Inference Serving System
Francisco Romero, Qian Li, Neeraja J. Yadwadkar +1
Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain challenges at large scales. Developers must manually search through thousands of…
A New Frontier for Pull-Based Graph Processing
Samuel Grossman, Christos Kozyrakis
The trade-off between pull-based and push-based graph processing engines is well-understood. On one hand, pull-based engines can achieve higher throughput because their workloads a…
Trevor: Automatic configuration and scaling of stream processing pipelines
Manu Bansal, Eyal Cidon, Arjun Balasingam +3
Operating a distributed data stream processing workload efficiently at scale is hard. The operator of the workload must parallelize and lay out tasks of the workload with resources…
Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators
Xuan Yang, Mingyu Gao, Qiaoyi Liu +9
We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops o…
Learning Memory Access Patterns
Milad Hashemi, Kevin Swersky, Jamie A. Smith +5
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recen…
AppSwitch: Resolving the Application Identity Crisis
Dinesh Subhraveti, Sri Goli, Serge Hallyn +2
Networked applications traditionally derive their identity from the identity of the host on which they run. The default application identity acquired from the host results in subtl…
Generating Configurable Hardware from Parallel Patterns
Raghu Prabhakar, David Koeplinger, Kevin Brown +4
In recent years the computing landscape has seen an in- creasing shift towards specialized accelerators. Field pro- grammable gate arrays (FPGAs) are particularly promising as they…