High-performance computing: Transitioning from Instruction-Level Parallelism to heterogeneous hybrid architectures

Abstract

This paper delves into the shift from Instruction-Level Parallelism (ILP) to Heterogeneous Hybrid Parallel Computing in the quest for optimized performance processing. It sheds light on the constraints of ILP, emphasizing how these shortcomings have catalyzed a move toward the more adaptable and proficient framework of heterogeneous hybrid computing. This transformation's advantages are explored across diverse applications, notably in deep learning, cloud computing, data centers, and mobile SoCs. Additionally, the study underscores emerging architectures and innovations of this era, including many-core processors, FPGA-driven accelerators, and an assortment of software tools and libraries. While heterogeneous hybrid computing offers a promising horizon, it isn't without challenges. This paper brings to the fore issues like restricted adaptability, steep development costs, software compatibility hurdles, the absence of a standardized programming model, and vendor reliance. Through this in-depth exploration, our aim is to present a holistic snapshot of the present and potential future of high-performance processing.