6 citations · 11 across the 5 of their papers we have counts for
7 papers
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs
Diego Didona, Nikolas Ioannou, Radu Stoica +1
Solid-state drives (SSDs) are extensively used to deploy persistent data stores, as they provide low latency random access, high write throughput, high data density, and low cost.…
Compiling Neural Networks for a Computational Memory Accelerator
Kornilios Kourtis, Martino Dazzi, Nikolas Ioannou +3
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computation…
Safe and Efficient Remote Application Code Execution on Disaggregated NVM Storage with eBPF
Kornilios Kourtis, Animesh Trivedi, Nikolas Ioannou
With rapid improvements in NVM storage devices, the performance bottleneck is gradually shifting to the network, thus giving rise to the notion of "data movement wall". To reduce t…
Cichlid: Explicit physical memory management for large machines
Simon Gerber, Gerd Zellweger, Reto Achermann +4
In this paper, we rethink how an OS supports virtual memory. Classical VM is an opaque abstraction of RAM, backed by demand paging. However, most systems today (from phones to data…
Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
Michael Kaufmann, Kornilios Kourtis, Celestine Mendler-Dünner +2
Distributed machine learning training is one of the most common and important workloads running on data centers today, but it is rarely executed alone. Instead, to reduce costs, co…
Parallel training of linear models without compromising convergence
Nikolas Ioannou, Celestine Dünner, Kornilios Kourtis +1
In this paper we analyze, evaluate, and improve the performance of training generalized linear models on modern CPUs. We start with a state-of-the-art asynchronous parallel trainin…