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Section: New Results
A New Framework for Evaluating Straggler Detection Mechanisms in MapReduce
In this work [10] we present a new framework for evaluating straggler detection mechanisms in MapReduce. We then show how to use it efficiently.
Application Domains
New Software and Platforms
New Results
- Management of heterogeneous and non-volatile memories in HPC
- Modeling and Visualizing Many-core HPC Platforms
- Co-scheduling HPC workloads on cache-partitioned CMP platforms
- Modeling High-throughput Applications for in situ Analytics
- Modeling Non-Uniform Memory Access and Heterogeneous Memories on Large Compute Nodes with the Cache-Aware Roofline Model
- Statistical Learning for Task and Data Placement in NUMA Architecture
- On-the-fly scheduling vs. reservation-based scheduling for unpredictable workflows
- Scheduling strategies for stochastic jobs
- Online Prediction of Network Utilization
- An Introspection Monitoring Library
- Tag matching in constant time
- Dynamic broadcasts in StarPU/NewMadeleine
- Task based asynchronous MPI collectives optimisation
- Dynamic placement of progress thread for overlapping MPI non-blocking collectives on manycore processor
- Dynamic placement of Hybrid MPI +X coupled applications
- Scheduling on Two Unbounded Resources with Communication Costs
- H-Revolve: A Framework for Adjoint Computation on Synchrone Hierarchical Platforms
- Sizing and Partitioning Strategies for Burst-Buffers to Reduce IO Contention
- Optimal Memory-aware Backpropagation of Deep Join Networks
- Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory
- I/O scheduling strategy for HPC applications
- A New Framework for Evaluating Straggler Detection Mechanisms in MapReduce
- Clarification of the MPI semantics
- Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography