8 citations · 35 across the 10 of their papers we have counts for
11 papers
An Elastic Ephemeral Datastore using Cheap, Transient Cloud Resources
Malte Brodmann, Nikolas Ioannou, Bernard Metzler +2
Spot instances are virtual machines offered at 60-90% lower cost that can be reclaimed at any time, with only a short warning period. Spot instances have already been used to signi…
Towards a General Framework for ML-based Self-tuning Databases
Thomas Schmied, Diego Didona, Andreas Döring +2
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimizatio…
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.…
SnapBoost: A Heterogeneous Boosting Machine
Thomas Parnell, Andreea Anghel, Malgorzata Lazuka +5
Modern gradient boosting software frameworks, such as XGBoost and LightGBM, implement Newton descent in a functional space. At each boosting iteration, their goal is to find the ba…
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…
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
Nikolas Ioannou, Celestine Mendler-Dünner, Thomas Parnell
In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability. We start by studying a state-o…
Breadth-first, Depth-next Training of Random Forests
Andreea Anghel, Nikolas Ioannou, Thomas Parnell +3
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures. An exact, state-of-the-art binary decision tree b…
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…
Snap ML: A Hierarchical Framework for Machine Learning
Celestine Dünner, Thomas Parnell, Dimitrios Sarigiannis +5
We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learn…
Elevating commodity storage with the SALSA host translation layer
Nikolas Ioannou, Kornilios Kourtis, Ioannis Koltsidas
To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices empl…