42 citations · 57 across the 7 of their papers we have counts for
9 papers
Unifying In-Memory Data Analytics through Sparse Compilation
Anand Jayarajan, Gennady Pekhimenko
As modern data analytics workloads become increasingly heterogeneous and hardware-intensive, achieving efficient multi-core performance across diverse applications remains an open…
SpecBench: Evaluating Specification-Level Reasoning for Software Engineering LLM Agents
Grant Hamblin, Kevin Song, Zhanda Zhu +4
Software engineering (SWE) agents are transitioning from code generation to full software development lifecycle automation. A critical phase in this lifecycle is specification desi…
What Limits Agentic Systems Efficiency?
Song Bian, Minghao Yan, Anand Jayarajan +2
Large Language Models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated strong reasoning capabilities. To further enhance LLM capabilities, recent agentic systems, such…
Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Kevin Song, Anand Jayarajan, Yaoyao Ding +4
Large Language Models (LLMs) agents augmented with domain tools promise to autonomously execute complex tasks requiring human-level intelligence, such as customer service and digit…
Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning Workloads
Wei Zhao, Anand Jayarajan, Gennady Pekhimenko
GPU underutilization is a significant concern in many production deep learning clusters, leading to prolonged job queues and increased operational expenses. A promising solution to…
TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization
Anand Jayarajan, Wei Zhao, Yudi Sun +1
Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit d…
LifeStream: A High-Performance Stream Processing Engine for Periodic Streams
Anand Jayarajan, Kimberly Hau, Andrew Goodwin +1
Hospitals around the world collect massive amounts of physiological data from their patients every day. Recently, there has been an increase in research interest to subject this da…
FPRaker: A Processing Element For Accelerating Neural Network Training
Omar Mohamed Awad, Mostafa Mahmoud, Isak Edo +5
We present FPRaker, a processing element for composing training accelerators. FPRaker processes several floating-point multiply-accumulation operations concurrently and accumulates…
Priority-based Parameter Propagation for Distributed DNN Training
Anand Jayarajan, Jinliang Wei, Garth Gibson +2
Data parallel training is widely used for scaling distributed deep neural network (DNN) training. However, the performance benefits are often limited by the communication-heavy par…