346 citations · 596 across the 18 of their papers we have counts for
5 papers · 1 filter
Characterizing Deep-Learning I/O Workloads in TensorFlow
Steven W. D. Chien, Stefano Markidis, Chaitanya Prasad Sishtla +4
The performance of Deep-Learning (DL) computing frameworks rely on the performance of data ingestion and checkpointing. In fact, during the training, a considerable high number of…
PolyPIC: the Polymorphic-Particle-in-Cell Method for Fluid-Kinetic Coupling
Stefano Markidis, Vyacheslav Olshevsky, Chaitanya Prasad Sishtla +3
Particle-in-Cell (PIC) methods are widely used computational tools for fluid and kinetic plasma modeling. While both the fluid and kinetic PIC approaches have been successfully use…
The SAGE Project: a Storage Centric Approach for Exascale Computing
Sai Narasimhamurthy, Nikita Danilov, Sining Wu +8
SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a B…
Exploring Scientific Application Performance Using Large Scale Object Storage
Steven Wei-der Chien, Stefano Markidis, Rami Karim +2
One of the major performance and scalability bottlenecks in large scientific applications is parallel reading and writing to supercomputer I/O systems. The usage of parallel file s…
NVIDIA Tensor Core Programmability, Performance & Precision
Stefano Markidis, Steven Wei Der Chien, Erwin Laure +2
The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called "Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVI…