[2] SHMT takes things a step further, identifying subtasks that can run independently of others to the appropriate processor type, allow even better parallelism.
Thus the fundamental breakthrough is to keep more processors working simultaneously, reducing time and energy costs.
[2] Researchers tested the concept using a typical smartphone configuration tweaked so that it resembled a data center server.
[1] The hardware was Nvidia's Jetson Nano module containing a quad-core ARM Cortex-A57 processor (CPU) and 128 Maxwell architecture GPU cores.
[1] Compared to a conventional system performance increased by 1.95X boost, while energy consumption was reduced by 51%, on a range of benchmarks, including Black–Scholes, DCT8X8, DWT, FFT, Histogram, Hotspot, Laplacian, MF, Sobel, SRAD, and GMEAN.