7 citations · 7 across the 1 of their papers we have counts for
3 papers
cs.DB2019
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Peter Kraft, Daniel Kang, Deepak Narayanan +3
Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss cr…
cs.DC2018
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…
cs.DC2017★ 7 cited
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…