346 citations · 596 across the 18 of their papers we have counts for
18 papers
Designing Transport-Level Encryption for Datacenter Networks
Tianyi Gao, Xinshu Ma, Suhas Narreddy +3
Cloud applications need network data encryption to isolate from other tenants and protect their data from potential eavesdroppers in the network infrastructure. This paper presents…
ParaLog: Consistent Host-side Logging for Parallel Checkpoints
Steven W. D. Chien, Kento Sato, Artur Podobas +3
Output-intensive scientific applications are highly sensitive to low storage throughput. While existing scientific application stacks are optimized for traditional High-Performance…
Higgs Boson Classification: Brain-inspired BCPNN Learning with StreamBrain
Martin Svedin, Artur Podobas, Steven W. D. Chien +1
One of the most promising approaches for data analysis and exploration of large data sets is Machine Learning techniques that are inspired by brain models. Such methods use alterna…
Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers
Martin Svedin, Steven W. D. Chien, Gibson Chikafa +2
For many, Graphics Processing Units (GPUs) provides a source of reliable computing power. Recently, Nvidia introduced its 9th generation HPC-grade GPUs, the Ampere 100, claiming si…
StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
Artur Podobas, Martin Svedin, Steven W. D. Chien +5
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other --…
tf-Darshan: Understanding Fine-grained I/O Performance in Machine Learning Workloads
Steven W. D. Chien, Artur Podobas, Ivy B. Peng +1
Machine Learning applications on HPC systems have been gaining popularity in recent years. The upcoming large scale systems will offer tremendous parallelism for training through G…
sputniPIC: an Implicit Particle-in-Cell Code for Multi-GPU Systems
Steven W. D. Chien, Jonas Nylund, Gabriel Bengtsson +3
Large-scale simulations of plasmas are essential for advancing our understanding of fusion devices, space, and astrophysical systems. Particle-in-Cell (PIC) codes have demonstrated…
Performance Evaluation of Advanced Features in CUDA Unified Memory
Steven W. D. Chien, Ivy B. Peng, Stefano Markidis
CUDA Unified Memory improves the GPU programmability and also enables GPU memory oversubscription. Recently, two advanced memory features, memory advises and asynchronous prefetch,…
Automated classification of plasma regions using 3D particle energy distributions
Vyacheslav Olshevsky, Yuri V. Khotyaintsev, Ahmad Lalti +8
We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural netw…
Posit NPB: Assessing the Precision Improvement in HPC Scientific Applications
Steven W. D. Chien, Ivy B. Peng, Stefano Markidis
Floating-point operations can significantly impact the accuracy and performance of scientific applications on large-scale parallel systems. Recently, an emerging floating-point for…
Multi-GPU Acceleration of the iPIC3D Implicit Particle-in-Cell Code
Chaitanya Prasad Sishtla, Steven W. D. Chien, Vyacheslav Olshevsky +2
iPIC3D is a widely used massively parallel Particle-in-Cell code for the simulation of space plasmas. However, its current implementation does not support execution on multiple GPU…
TensorFlow Doing HPC
Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky +3
TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initial…
Particle-in-Cell Simulations of Plasma Dynamics in Cometary Environment
Chaitanya Prasad Sishtla, Vyacheslav Olshevsky, Steven W. D. Chien +2
We perform and analyze global Particle-in-Cell (PIC) simulations of the interaction between solar wind and an outgassing comet with the goal of studying the plasma kinetic dynamics…
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…