New Method Accelerates Computer Systems While Cutting Energy Use
Researchers from the University of California, Riverside have demonstrated a way to speed up computer systems significantly without buying new hardware. The breakthrough focuses on smarter use of existing components to boost performance and reduce energy consumption. The findings were published by a respected engineering publication, IEEE.
The approach centers on concurrent and heterogeneous multithreading, abbreviated SHMT. The idea is to exploit the different kinds of processors already inside modern machines—graphics processing units, central processing units, and tensor processors used in artificial intelligence—so tasks are handled by the most suitable chip at the right moment.
In their tests, the team set up a system with an ARM Cortex-A57 CPU, an Nvidia GPU, and a Google Edge Tensor Processor. By coordinating work across these diverse processors, they observed the code run about 1.95 times faster and energy use drop by roughly half. The researchers emphasize that traditional programming models tend to rely on the single most efficient processor for each code region, missing opportunities to leverage a heterogeneous hardware mix more comprehensively.
Despite the impressive gains, the team notes substantial hurdles remain. A key challenge is correctly partitioning computing tasks so that different processor types handle the right portions and then recombining results without introducing slowdowns. Because of these integration difficulties, SHMT is not expected to see widespread deployment in the near term.
Earlier statements from other scientists pointed toward the goal of ultra-fast AI computing through highly specialized chips. The current UC Riverside work complements those efforts by showing how existing devices can be coordinated more effectively to deliver speed and efficiency gains without new hardware infrastructure.
The research highlights a broader trend in high-performance computing: performance gains increasingly depend on software strategies that match workload characteristics to heterogeneous hardware. This alignment can deliver better throughput while keeping energy costs in check, an important consideration for data centers, edge computing, and mobile devices alike. The implications extend to developers and engineers who design software that must run efficiently across different platforms and architectures. As SHMT develops, stakeholders will watch for evolving toolchains and programming models that make this approach easier to implement in real-world systems. (IEEE)