The memory-bounded speedup model and its impacts in computing
With the surge of big data applications and the worsening of the memory-wall problem, the
memory system, instead of the computing unit, becomes the commonly recognized major …
memory system, instead of the computing unit, becomes the commonly recognized major …
Dlio: A data-centric benchmark for scientific deep learning applications
Deep learning has been shown as a successful method for various tasks, and its popularity
results in numerous open-source deep learning software tools. Deep learning has been …
results in numerous open-source deep learning software tools. Deep learning has been …
Characterizing machine learning i/o workloads on leadership scale hpc systems
High performance computing (HPC) is no longer solely limited to traditional workloads such
as simulation and modeling. With the increase in the popularity of machine learning (ML) …
as simulation and modeling. With the increase in the popularity of machine learning (ML) …
Study on tiered storage algorithm based on heat correlation of astronomical data
XC Ye, HL Zhang, J Wang, YZ Zhang, X Du… - Frontiers in Astronomy …, 2024 - frontiersin.org
With the surge in astronomical data volume, modern astronomical research faces significant
challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical …
challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical …
Data Flow Lifecycles for Optimizing Workflow Coordination
A critical performance challenge in distributed scientific workflows is coordinating tasks and
data flows on distributed resources. To guide these decisions, this paper introduces data …
data flows on distributed resources. To guide these decisions, this paper introduces data …
HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems
Data-intensive applications are generating massive amounts of data which is stored on
cloud computing platforms where distributed file systems are utilized for storage at the back …
cloud computing platforms where distributed file systems are utilized for storage at the back …
I/O performance analysis of machine learning workloads on leadership scale supercomputer
The popularity of machine learning technologies and frameworks has led to an increasingly
large number of machine learning workloads running on high-performance computing …
large number of machine learning workloads running on high-performance computing …
Application and user-specific data prefetching and parallel read algorithms for distributed file systems
Cloud computing systems are widely used to deploy big data-based applications because of
their high storage and computation capacity. The key component for storage in cloud …
their high storage and computation capacity. The key component for storage in cloud …
Efficient Prefetching and Client-Side Caching Algorithms for Improving the Performance of Read Operations in Distributed File Systems
Modern web applications are deployed in cloud computing systems because they support
unlimited storage and computing power. One of the main back-end storage components of …
unlimited storage and computing power. One of the main back-end storage components of …
Distributed file systembased optimization algorithm
UL Soundharya, G Vadivu, GK Chaitanya - Wireless Networks, 2024 - Springer
Database engines and file systems have been using prefetching and caching technologies
for decades to enhance the performance of I/O-intensive applications. When future data …
for decades to enhance the performance of I/O-intensive applications. When future data …