56 citations · 167 across the 22 of their papers we have counts for
24 papers
Workload-Aware DRAM Error Prediction using Machine Learning
Lev Mukhanov, Konstantinos Tovletoglou, Hans Vandierendonck +2
The aggressive scaling of technology may have helped to meet the growing demand for higher memory capacity and density, but has also made DRAM cells more prone to errors. Such a re…
Implementing Efficient Message Logging Protocols as MPI Application Extensions
Kiril Dichev, Dimitrios S. Nikolopoulos
Message logging protocols are enablers of local rollback, a more efficient alternative to global rollback, for fault tolerant MPI applications. Until now, message logging MPI imple…
RADS: Real-time Anomaly Detection System for Cloud Data Centres
Sakil Barbhuiya, Zafeirios Papazachos, Peter Kilpatrick +1
Cybersecurity attacks in Cloud data centres are increasing alongside the growth of the Cloud services market. Existing research proposes a number of anomaly detection systems for d…
DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
Nan Wang, Michail Matthaiou, Dimitrios S. Nikolopoulos +1
Multi-tenancy in resource-constrained environments is a key challenge in Edge computing. In this paper, we develop 'DYVERSE: DYnamic VERtical Scaling in Edge' environments, which i…
VEBO: A Vertex- and Edge-Balanced Ordering Heuristic to Load Balance Parallel Graph Processing
Jiawen Sun, Hans Vandierendonck, Dimitrios S. Nikolopoulos
Graph partitioning drives graph processing in distributed, disk-based and NUMA-aware systems. A commonly used partitioning goal is to balance the number of edges per partition in c…
Energy-efficient localised rollback after failures via data flow analysis
Kiril Dichev, Kirk Cameron, Dimitrios Nikolopoulos
Exascale systems will suffer failures hourly. HPC programmers rely mostly on application-level checkpoint and a global rollback to recover. In recent years, techniques reducing the…
TAPAS: Train-less Accuracy Predictor for Architecture Search
R. Istrate, F. Scheidegger, G. Mariani +3
In recent years an increasing number of researchers and practitioners have been suggesting algorithms for large-scale neural network architecture search: genetic algorithms, reinfo…
Incremental Training of Deep Convolutional Neural Networks
Roxana Istrate, Adelmo Cristiano Innocenza Malossi, Costas Bekas +1
We propose an incremental training method that partitions the original network into sub-networks, which are then gradually incorporated in the running network during the training p…
Intra-node Memory Safe GPU Co-Scheduling
Carlos Reano, Federico Silla, Dimitrios S. Nikolopoulos +1
GPUs in High-Performance Computing systems remain under-utilised due to the unavailability of schedulers that can safely schedule multiple applications to share the same GPU. The r…
Power Modelling for Heterogeneous Cloud-Edge Data Centers
Kai Chen, Blesson Varghese, Peter Kilpatrick +1
Existing power modelling research focuses not on the method used for developing models but rather on the model itself. This paper aims to develop a method for deploying power model…
Edge-as-a-Service: Towards Distributed Cloud Architectures
Blesson Varghese, Nan Wang, Jianyu Li +1
We present an Edge-as-a-Service (EaaS) platform for realising distributed cloud architectures and integrating the edge of the network in the computing ecosystem. The EaaS platform…
ENORM: A Framework For Edge NOde Resource Management
Nan Wang, Blesson Varghese, Michail Matthaiou +1
Current computing techniques using the cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This raises the need for fog comput…
Dependency-Aware Rollback and Checkpoint-Restart for Distributed Task-Based Runtimes
Kiril Dichev, Herbert Jordan, Konstantinos Tovletoglou +4
With the increase in compute nodes in large compute platforms, a proportional increase in node failures will follow. Many application-based checkpoint/restart (C/R) techniques have…
Feasibility of Fog Computing
Blesson Varghese, Nan Wang, Dimitrios S. Nikolopoulos +1
As billions of devices get connected to the Internet, it will not be sustainable to use the cloud as a centralised server. The way forward is to decentralise computations away from…
Challenges and Opportunities in Edge Computing
Blesson Varghese, Nan Wang, Sakil Barbhuiya +2
Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model pla…
BDDT-SCC: A Task-parallel Runtime for Non Cache-Coherent Multicores
Alexandros Labrineas, Polyvios Pratikakis, Dimitrios S. Nikolopoulos +1
This paper presents BDDT-SCC, a task-parallel runtime system for non cache-coherent multicore processors, implemented for the Intel Single-Chip Cloud Computer. The BDDT-SCC runtime…
Myrmics: Scalable, Dependency-aware Task Scheduling on Heterogeneous Manycores
Spyros Lyberis, Polyvios Pratikakis, Iakovos Mavroidis +1
Task-based programming models have become very popular, as they offer an attractive solution to parallelize serial application code with task and data annotations. They usually dep…
TwinCG: Dual Thread Redundancy with Forward Recovery for Conjugate Gradient Methods
Kiril Dichev, Dimitrios S. Nikolopoulos
Even though iterative solvers like the Conjugate Gradients method (CG) have been studied for over fifty years, fault tolerance for such solvers has seen much attention in recent ye…
Energy Optimization of Memory Intensive Parallel workloads
Chhaya Trehan, Hans Vandierendonck, Georgios Karakonstantis +1
Energy consumption is an important concern in modern multicore processors. The energy consumed during the execution of an application can be minimized by tuning the hardware state…
ALEA: Fine-grain Energy Profiling with Basic Block Sampling
Lev Mukhanov, Dimitrios S. Nikolopoulos, Bronis R. de Supinski
Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand ho…
Evaluating Asymmetric Multicore Systems-on-Chip using Iso-Metrics
Charalampos Chalios, Dimitrios S. Nikolopoulos, Enrique S. Quintana-Orti
The end of Dennard scaling has pushed power consumption into a first order concern for current systems, on par with performance. As a result, near-threshold voltage computing (NTVC…
Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics
Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed +3
We present a mathematically rigorous Quality-of-Service (QoS) metric which relates the achievable quality of service metric (QoS) for a real-time analytics service to the server en…
Methods and Metrics for Fair Server Assessment under Real-Time Financial Workloads
Giorgis Georgakoudis, Charles J. Gillan, Ahmed Sayed +3
Energy efficiency has been a daunting challenge for datacenters. The financial industry operates some of the largest datacenters in the world. With increasing energy costs and the…
A Programming Model and Runtime System for Significance-Aware Energy-Efficient Computing
Vassilis Vassiliadis, Konstantinos Parasyris, Charalambos Chalios +5
Reducing energy consumption is one of the key challenges in computing technology. One factor that contributes to high energy consumption is that all parts of the program are consid…