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This study presents a comprehensive benchmarking analysis of the Arm-based AmpereOne A192-32X CPU, a high-performance but low power processor designed for cloud-native workloads characterized by high core occupancy, imperfectly-vectorized or even pure scalar software, limited need for high floating-point performance, and, increasingly, AI inference. These traits also characterize much of academic research computing. Hence a thorough investigation of this novel CPU seeking to characterize its strengths and weaknesses on academic workloads, including traditional HPC codes for which it was not designed, will shed light on its relevance in a research setting. We report comparative analyses with contemporary CPUs (Intel Sapphire Rapids, AMD EPYC, NVIDIA Grace-Grace) and illustrate AmpereOne’s architectural advantages in handling parallel workloads and optimizing power consumption. The CPUs are compared in terms of performance and power consumption using a wide range of applications covering different workloads and disciplines. 

Abstract The landscape of high performance computing (HPC) has witnessed exponential growth in processor diversity, architectural complexity, and performance scalability. With an ever-increasing demand for faster and more efficient computing solutions to address an array of scientific, engineering, and societal challenges, the selection of processors for specific applications becomes paramount. Achieving optimal performance requires a deep understanding of how diverse processors interact with diverse workloads, making benchmarking a fundamental practice in the field of HPC. Here, we present preliminary results observed over such benchmarks and applications and a comparison of Intel Sapphire Rapids and Skylake-X, AMD Milan, and Fujitsu A64FX processors in terms of runtime performance, memory bandwidth utilization, and energy consumption. The examples focus specifically on the Sapphire Rapids processor with and without high-bandwidth memory (HBM). An additional case study reports the performance gains from using Intel’s Advanced Matrix Extensions (AMX) instructions, and how they along with HBM can be leveraged to accelerate AI workloads. These initial results aim to give a rough comparison of the processors rather than a detailed analysis and should prove timely and relevant for researchers who may be interested in using Sapphire Rapids for their scientific workloads.