Balancing Energy Efficiency and Portability: Assessing Domain-Specific Languages in Edge Platforms

Edge computing stands as a crucial component of the IoT revolution. IoT benefits significantly from reduced latency, enhancing real-time applications, and time-sensitive processes. However, since edge platforms must be energy-efficient while maintaining high performance in real-time environments, exploiting accelerators becomes pivotal to reduce energy consumption while increasing performance. CUDA or HIP languages are widely used to program GPUs; however, they are limited to specific vendor platforms. In contrast, solutions such as SYCL or Kokkos could benefit from their ability to run across parallel multi-architecture.This paper explores the trade-offs of CUDA, SYCL, and Kokkos in three Edge platforms: Jetson AGX Orin, Jetson Orin Nano, and UP Squared Pro 7000 (Intel). The work conducts an analysis of the power modes of each platform and compares CUDA, SYCL, and Kokkos on those platforms.The experimental results revealed that, on average, there is an increased level of energy consumption compared to native solutions. Specifically, Kokkos demonstrated a 25% higher demand, whereas SYCL exhibited an average increase of 11%. These figures underscore the trade-off that programmers face in pursuit of achieving code portability when most performant solutions are non-crucial.