Impact of Workload Distribution on Energy Consumption, Performance, and Reliability of Heterogeneous Devices

Devices integrating cores of different nature in the same chip achieve very high computation efficiency by reducing the power consumption and latencies of moving data from a chip to an external device. Heterogeneous devices are commonly used in high performance computing applications and, lately, their market has expanded from portable and gaming to safety-critical applications. In this paper, we evaluate how the collaborative workload distribution impact the energy consumption, performance, and reliability of heterogeneous devices. Then, we use the Energy-Delay-Fit Product (EDFP) to evaluate the trade off between the measured metrics and find how they correlate. To perform the proposed study we consider AMD Accelerated Processing Units (APUs) that embed a CPU and a GPU. We run four applications, each one representing an algorithm class, gradually distributing the workload from the CPU to the GPU and measuring both the energy consumption and execution time. Then, we take advantage of accelerated neutron beams to measure the realistic error rates of the different workload distributions. As we show in the paper, energy consumption and execution time are mold by the same trend while FIT rates highly depend on algorithm class and workload distribution. Additionally, we found that execution time is the most influencing factor for the device EDFP. The application EDFP varies of up tp 6 orders of magnitude depending on the workload distribution. An unwise configuration can then jeopardize the device efficiency and reliability.