2.3k citations · 4.7k across the 64 of their papers we have counts for
9 papers · 1 filter
MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
Shiyi Cao, Shu Liu, Tyler Griggs +6
Efficient deployment of large language models, particularly Mixture of Experts (MoE), on resource-constrained platforms presents significant challenges, especially in terms of comp…
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP
Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid +5
Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shel…
Don't Give Up on Large Optimization Problems; POP Them!
Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid +2
Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shel…
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka +2
Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been increasingly deployed to train deep learning models. These accelerators exhibit heterogeneous perform…
Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations
Shoumik Palkar, Matei Zaharia
Data movement between main memory and the CPU is a major bottleneck in parallel data-intensive applications. In response, researchers have proposed using compilers and intermediate…
Beyond Data and Model Parallelism for Deep Neural Networks
Zhihao Jia, Matei Zaharia, Alex Aiken
The computational requirements for training deep neural networks (DNNs) have grown to the point that it is now standard practice to parallelize training. Existing deep learning sys…
Weld: Rethinking the Interface Between Data-Intensive Applications
Shoumik Palkar, James Thomas, Deepak Narayanan +7
Data analytics applications combine multiple functions from different libraries and frameworks. Even when each function is optimized in isolation, the performance of the combined a…
Making Caches Work for Graph Analytics
Yunming Zhang, Vladimir Kiriansky, Charith Mendis +2
Modern hardware systems are heavily underutilized when running large-scale graph applications. While many in-memory graph frameworks have made substantial progress in optimizing th…
Matrix Computations and Optimization in Apache Spark
Reza Bosagh Zadeh, Xiangrui Meng, Aaron Staple +6
We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark provides abstractions and implementations for distributed matric…