Napel: Near-memory computing application performance prediction via ensemble learning
The cost of moving data between the memory/storage units and the compute units is a major
contributor to the execution time and energy consumption of modern workloads in …
contributor to the execution time and energy consumption of modern workloads in …
Predicting the performance-cost trade-off of applications across multiple systems
A Nassereldine, S Diab, M Baydoun… - 2023 IEEE/ACM …, 2023 - ieeexplore.ieee.org
In modern computing environments, users may have multiple systems accessible to them
such as local clusters, private clouds, or public clouds. This abundance of choices makes it …
such as local clusters, private clouds, or public clouds. This abundance of choices makes it …
Enabling scalable and adaptive machine learning training via serverless computing on public cloud
In today's production machine learning (ML) systems, models are continuously trained,
improved, and deployed. ML design and training are becoming a continuous workflow of …
improved, and deployed. ML design and training are becoming a continuous workflow of …
Smlt: A serverless framework for scalable and adaptive machine learning design and training
In today's production machine learning (ML) systems, models are continuously trained,
improved, and deployed. ML design and training are becoming a continuous workflow of …
improved, and deployed. ML design and training are becoming a continuous workflow of …
Predicting cloud performance for HPC applications before deployment
To reduce the capital investment required to acquire and maintain a high performance
computing cluster, today many HPC users are moving to cloud. When deploying an …
computing cluster, today many HPC users are moving to cloud. When deploying an …
AI gauge: Runtime estimation for deep learning in the cloud
Major cloud providers, including IBM Cloud, Amazon Web Services, Microsoft Azure, and
Google Cloud, offer services to train, debug, store, and deploy machine learning models at …
Google Cloud, offer services to train, debug, store, and deploy machine learning models at …
Nmpo: Near-memory computing profiling and offloading
Real-world applications are now processing big-data sets, often bottlenecked by the data
movement between the compute units and the main memory. Near-memory computing …
movement between the compute units and the main memory. Near-memory computing …
Distributed matrix multiplication performance estimator for machine learning jobs in cloud computing
Matrix multiplication is an important kernel task in many machine learning algorithms. As the
size of input datasets increases, multiple workloads are analyzed in large-scale distributed …
size of input datasets increases, multiple workloads are analyzed in large-scale distributed …
Ndp-rank: Prediction and ranking of ndp systems performance using machine learning
The near-data processing (NDP) paradigm has recently gained popularity as a promising
method for mitigating the memory wall challenges of future computing systems. Modern 3D …
method for mitigating the memory wall challenges of future computing systems. Modern 3D …
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning
G Singha, D Diamantopoulosb… - 2022 IEEE 40th …, 2022 - ieeexplore.ieee.org
Machine learning has recently gained traction as a way to overcome the slow accelerator
generation and implementation process on an FPGA. It can be used to build performance …
generation and implementation process on an FPGA. It can be used to build performance …