activity
20182023
most citedScaling Distributed Training with Adaptive Summation

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DC2020

Synthesizing Optimal Collective Algorithms

Zixian Cai, Zhengyang Liu, Saeed Maleki +4

Collective communication algorithms are an important component of distributed computation. Indeed, in the case of deep-learning, collective communication is the Amdahl's bottleneck…

cs.DC20201 cited

Scaling Distributed Training with Adaptive Summation

Saeed Maleki, Madan Musuvathi, Todd Mytkowicz +5

Stochastic gradient descent (SGD) is an inherently sequential training algorithm--computing the gradient at batch depends on the model parameters learned from batch . Prio…

cs.CR2019

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi +3

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations…

cs.FL2019

Succinct Determinisation of Counting Automata via Sphere Construction (Technical Report)

Lukáš Holík, Ondřej Lengál, Olli Saarikivi +3

We propose an efficient algorithm for determinising counting automata (CAs), i.e., finite automata extended with bounded counters. The algorithm avoids unfolding counters into cont…

cs.LG2018

CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs

Roshan Dathathri, Olli Saarikivi, Hao Chen +5

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enabl…