222 citations · 397 across the 21 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…