173 citations · 337 across the 19 of their papers we have counts for
20 papers
Framing AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset
Grigori Fursin, Daniel Altunay
Existing AI system benchmarks such as MLPerf often struggle to keep pace with the rapidly evolving AI landscape, making it difficult to support informed deployment, optimization, a…
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Arya Tschand, Arun Tejusve Raghunath Rajan, Sachin Idgunji +23
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmark…
Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments
Grigori Fursin
This white paper introduces my educational community initiative to learn how to run AI, ML and other emerging workloads in the most efficient and cost-effective way across diverse…
A Community Roadmap for Scientific Workflows Research and Development
Rafael Ferreira da Silva, Henri Casanova, Kyle Chard +26
The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development
Rafael Ferreira da Silva, Henri Casanova, Kyle Chard +55
Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflow…
Workflows Community Summit: Bringing the Scientific Workflows Community Together
Rafael Ferreira da Silva, Henri Casanova, Kyle Chard +42
Scientific workflows have been used almost universally across scientific domains, and have underpinned some of the most significant discoveries of the past several decades. Many of…
Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common APIs
Grigori Fursin
This article provides the motivation and overview of the Collective Knowledge framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable component…
The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
Grigori Fursin
This article provides an overview of the Collective Knowledge technology (CK or cKnowledge). CK attempts to make it easier to reproduce ML&systems research, deploy ML models in pro…
CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking
Grigori Fursin, Herve Guillou, Nicolas Essayan
We present CodeReef - an open platform to share all the components necessary to enable cross-platform MLOps (MLSysOps), i.e. automating the deployment of ML models across diverse s…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
SysML'19 demo: customizable and reusable Collective Knowledge pipelines to automate and reproduce machine learning experiments
Grigori Fursin
Reproducing, comparing and reusing results from machine learning and systems papers is a very tedious, ad hoc and time-consuming process. I will demonstrate how to automate this pr…
A model-driven approach for a new generation of adaptive libraries
Marco Cianfriglia, Flavio Vella, Cedric Nugteren +2
Efficient high-performance libraries often expose multiple tunable parameters to provide highly optimized routines. These can range from simple loop unroll factors or vector sizes…
A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques
Grigori Fursin, Anton Lokhmotov, Dmitry Savenko +1
Developing efficient software and hardware has never been harder whether it is for a tiny IoT device or an Exascale supercomputer. Apart from the ever growing design and optimizati…
Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments
Thierry Moreau, Anton Lokhmotov, Grigori Fursin
Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time…
Collective Mind, Part II: Towards Performance- and Cost-Aware Software Engineering as a Natural Science
Grigori Fursin, Abdul Memon, Christophe Guillon +1
Nowadays, engineers have to develop software often without even knowing which hardware it will eventually run on in numerous mobile phones, tablets, desktops, laptops, data centers…
Proceedings of the 5th International Workshop on Adaptive Self-tuning Computing Systems 2015 (ADAPT'15)
Christophe Dubach, Grigori Fursin
This is the proceedings of the 5th International Workshop on Adaptive Self-tuning Computing Systems 2015 (ADAPT'15).
Collective Tuning Initiative
Grigori Fursin
Computing systems rarely deliver best possible performance due to ever increasing hardware and software complexity and limitations of the current optimization technology. Additiona…
Community-driven reviewing and validation of publications
Grigori Fursin, Christophe Dubach
In this report, we share our practical experience on crowdsourcing evaluation of research artifacts and reviewing of publications since 2008. We also briefly discuss encountered pr…
Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning
Grigori Fursin
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design an…