171 citations · 224 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee +4
PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines. Its pipeline parallel computing mod…
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…