Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Gokhale, Maya"'
Disaggregated memory breaks the boundary of monolithic servers to enable memory provisioning on demand. Using network-attached memory to provide memory expansion for memory-intensive applications on compute nodes can improve the overall memory utiliz
Externí odkaz:
http://arxiv.org/abs/2410.02599
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 an application's bottlenecks on memory access and guiding optimizatio
Externí odkaz:
http://arxiv.org/abs/2410.01514
Autor:
Gokhale, Maya, Gopalakrishnan, Ganesh, Mayo, Jackson, Nagarakatte, Santosh, Rubio-González, Cindy, Siegel, Stephen F.
This report is a digest of the DOE/NSF Workshop on Correctness in Scientific Computing (CSC'23) held on June 17, 2023, as part of the Federated Computing Research Conference (FCRC) 2023. CSC was conceived by DOE and NSF to address the growing concern
Externí odkaz:
http://arxiv.org/abs/2312.15640
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 have also highlighted memory underutilization. A promising and non-dis
Externí odkaz:
http://arxiv.org/abs/2308.14780
Autor:
Mudunuru, Maruti K., Ang, James A., Halappanavar, Mahantesh, Hammond, Simon D., Gokhale, Maya B., Hoe, James C., Krishna, Tushar, Sreepathi, Sarat S., Norman, Matthew R., Peng, Ivy B., Jones, Philip W.
Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (
Externí odkaz:
http://arxiv.org/abs/2304.03748
Current HPC systems provide memory resources that are statically configured and tightly coupled with compute nodes. However, workloads on HPC systems are evolving. Diverse workloads lead to a need for configurable memory resources to achieve high per
Externí odkaz:
http://arxiv.org/abs/2211.02682
The prediction accuracy of the deep neural networks (DNNs) after deployment at the edge can suffer with time due to shifts in the distribution of the new data. To improve robustness of DNNs, they must be able to update themselves to enhance their pre
Externí odkaz:
http://arxiv.org/abs/2203.11295
Data analytics applications transform raw input data into analytics-specific data structures before performing analytics. Unfortunately, such data ingestion step is often more expensive than analytics. In addition, various types of NVRAM devices are
Externí odkaz:
http://arxiv.org/abs/2108.07223
Processing large numbers of key/value lookups is an integral part of modern server databases and other "Big Data" applications. Prior work has shown that hash table based key/value lookups can benefit significantly from using a dedicated hardware loo
Externí odkaz:
http://arxiv.org/abs/2105.06594