Corpus ID: 237492051

Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms

  title={Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms},
  author={Mohammad Saleem},
Machine learning algorithms have enabled computers to predict things by learning from previous data. The data storage and processing power are increasing rapidly, thus increasing machine learning and Artificial intelligence applications. Much of the work is done to improve the accuracy of the models built in the past, with little research done to determine the computational costs of machine learning acquisitions. In this paper, I will proceed with this later research work and will make a… 


A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs
It is demonstrated how lightweight artificial neural networks (ANNs) can provide highly accurate performance predictions for a diverse set of applications thereby helping to improve heterogeneous scheduling efficiency and yields 25% to 31% throughput improvements over conventional heterogeneous schedulers for CPU and memory intensive applications.
Machine Learning-Based Approaches for Energy-Efficiency Prediction and Scheduling in Composite Cores Architectures
A systematic approach is described to predict the right configurations for running multithreaded workloads on the composite cores architecture by developing a machine learning-based approach to predict core type, voltage and frequency to maximize the energy-efficiency.
A Survey of Machine Learning Applied to Computer Architecture Design
Applied machine learning applied system-wide to simulation and run-time optimization, and in many individual components, including memory systems, branch predictors, networks-on-chip, and GPUs present a promising future for increasingly automated architectural design.
Evaluating architecture impact on system energy efficiency
It is argued it is the exact time to conduct an in-depth evaluation of the existing architecture designs to understand their impact on system energy efficiency and Turbo Boost is effective to accelerate the workload execution and further preserve the energy.
Estimation of energy consumption in machine learning
Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is stil
Cardiovascular disease dataset, Jan 2019
  • 2019
Cardiovascular disease dataset