346 citations · 833 across the 47 of their papers we have counts for
38 papers · 1 filter
VERA: Reinforcement Learning for Dynamic Memory Scaling of HPC Workloads in Kubernetes
Ade Pramono, Jie Ren, Ivy Peng
Memory over-provisioning results in resource underutilization when HPC workloads run on Kubernetes. The default Vertical Pod Autoscaler (VPA) cannot anticipate phase-driven memory…
Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs
Jacob Wahlgren, Gabin Schieffer, Ruimin Shi +4
Discrete GPUs are a cornerstone of HPC and data center systems, requiring management of separate CPU and GPU memory spaces. Unified Virtual Memory (UVM) has been proposed to ease t…
Inter-APU Communication on AMD MI300A Systems via Infinity Fabric: a Deep Dive
Gabin Schieffer, Jacob Wahlgren, Ruimin Shi +4
The ever-increasing compute performance of GPU accelerators drives up the need for efficient data movements within HPC applications to sustain performance. Proposed as a solution t…
ARM SVE Unleashed: Performance and Insights Across HPC Applications on Nvidia Grace
Ruimin Shi, Gabin Schieffer, Maya Gokhale +3
Vector architectures are essential for boosting computing throughput. ARM provides SVE as the next-generation length-agnostic vector extension beyond traditional fixed-length SIMD.…
ARC-V: Vertical Resource Adaptivity for HPC Workloads in Containerized Environments
Daniel Medeiros, Jeremy J. Williams, Jacob Wahlgren +2
Existing state-of-the-art vertical autoscalers for containerized environments are traditionally built for cloud applications, which might behave differently than HPC workloads with…
Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs
Jonah Ekelund, Stefano Markidis, Ivy Peng
Graphics Processing Units (GPUs) have become the standard in accelerating scientific applications on heterogeneous systems. However, as GPUs are getting faster, one potential perfo…
Kub: Enabling Elastic HPC Workloads on Containerized Environments
Daniel Medeiros, Jacob Wahlgren, Gabin Schieffer +1
The conventional model of resource allocation in HPC systems is static. Thus, a job cannot leverage newly available resources in the system or release underutilized resources durin…
A GPU-accelerated Molecular Docking Workflow with Kubernetes and Apache Airflow
Daniel Medeiros, Gabin Schieffer, Jacob Wahlgren +1
Complex workflows play a critical role in accelerating scientific discovery. In many scientific domains, efficient workflow management can lead to faster scientific output and broa…
Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores
Gabin Schieffer, Ivy Peng
In drug discovery, molecular docking aims at characterizing the binding of a drug-like molecule to a macromolecule. AutoDock-GPU, a state-of-the-art docking software, estimates the…
OpenCUBE: Building an Open Source Cloud Blueprint with EPI Systems
Ivy Peng, Martin Schulz, Utz-Uwe Haus +8
OpenCUBE aims to develop an open-source full software stack for Cloud computing blueprint deployed on EPI hardware, adaptable to emerging workloads across the computing continuum.…
Disaggregated Memory with SmartNIC Offloading: a Case Study on Graph Processing
Jacob Wahlgren, Gabin Schieffer, Maya Gokhale +2
Disaggregated memory breaks the boundary of monolithic servers to enable memory provisioning on demand. Using network-attached memory to provide memory expansion for memory-intensi…
Multi-level Memory-Centric Profiling on ARM Processors with ARM SPE
Samuel Miksits, Ruimin Shi, Maya Gokhale +3
High-end ARM processors are emerging in data centers and HPC systems, posing as a strong contender to x86 machines. Memory-centric profiling is an important approach for dissecting…
Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric
Gabin Schieffer, Ruimin Shi, Stefano Markidis +3
Modern GPU systems are constantly evolving to meet the needs of computing-intensive applications in scientific and machine learning domains. However, there is typically a gap betwe…
Harnessing Integrated CPU-GPU System Memory for HPC: a first look into Grace Hopper
Gabin Schieffer, Jacob Wahlgren, Jie Ren +2
Memory management across discrete CPU and GPU physical memory is traditionally achieved through explicit GPU allocations and data copy or unified virtual memory. The Grace Hopper S…
Understanding Layered Portability from HPC to Cloud in Containerized Environments
Daniel Medeiros, Gabin Schieffer, Jacob Wahlgren +1
Recent development in lightweight OS-level virtualization, containers, provides a potential solution for running HPC applications on the cloud platform. In this work, we focus on t…
Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations
Francieli Boito, Jim Brandt, Valeria Cardellini +24
Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve ef…
A Quantitative Approach for Adopting Disaggregated Memory in HPC Systems
Jacob Wahlgren, Gabin Schieffer, Maya Gokhale +1
Memory disaggregation has recently been adopted in data centers to improve resource utilization, motivated by cost and sustainability. Recent studies on large-scale HPC facilities…
Boosting the Performance of Object Tracking with a Half-Precision Particle Filter on GPU
Gabin Schieffer, Nattawat Pornthisan, Daniel Araújo de Medeiros +3
High-performance GPU-accelerated particle filter methods are critical for object detection applications, ranging from autonomous driving, robot localization, to time-series predict…
Leveraging HPC Profiling & Tracing Tools to Understand the Performance of Particle-in-Cell Monte Carlo Simulations
Jeremy J. Williams, David Tskhakaya, Stefan Costea +3
Large-scale plasma simulations are critical for designing and developing next-generation fusion energy devices and modeling industrial plasmas. BIT1 is a massively parallel Particl…
Evaluating Emerging CXL-enabled Memory Pooling for HPC Systems
Jacob Wahlgren, Maya Gokhale, Ivy B. Peng
Current HPC systems provide memory resources that are statically configured and tightly coupled with compute nodes. However, workloads on HPC systems are evolving. Diverse workload…
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…
Demystifying the Performance of HPC Scientific Applications on NVM-based Memory Systems
Ivy Peng, Kai Wu, Jie Ren +2
The emergence of high-density byte-addressable non-volatile memory (NVM) is promising to accelerate data- and compute-intensive applications. Current NVM technologies have lower pe…