A new computing approach called "simultaneous and heterogeneous multithreading (SHMT)" could potentially double the processing speed of devices like phones and laptops without replacing any components. This method allows different processing units to work on the same code region simultaneously, resulting in faster performance and 51% less energy consumption in tests. The approach could also reduce hardware costs, carbon emissions, and water usage in data centers, but further research is needed to determine practical implementation and specific use cases.
Researchers at the University of California, Riverside, have developed a new technology called simultaneous and heterogeneous multithreading (SHMT) that can potentially double the performance of existing PCs by enabling a single task to be carried out across multiple processors simultaneously, reducing workload per processor and energy usage. However, challenges remain in ensuring compatibility with different processor architectures before the technology can be brought to market.
Researchers from the University of California, Riverside have developed a system called simultaneous and heterogeneous multithreading (SHMT) that could potentially double the speed of existing computers without requiring hardware upgrades. By allowing tasks to run simultaneously across multiple processors, SHMT could significantly improve processing speed and reduce energy usage. However, the technology faces challenges in ensuring quality and precision in processor architectures before it can be widely implemented.
Researchers at the University of California, Riverside have developed simultaneous and heterogeneous multithreading (SHMT), a new process that could potentially double the speed of existing computers by taking advantage of multiple processors in modern devices. The innovative approach aims to increase efficiency and reduce energy use, with promising test results showing a 1.95 times faster execution of sample code and a 51 percent reduction in energy use. While still in the early stages, the research presents a new direction for improving processor efficiency and performance in smartphones, computers, and other gadgets.
