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The sedimentary bed morphology modulated by the wake flow of a wall-mounted flexible aquatic vegetation blade across various structural aspect ratios (AR=l/b, where l and b are the length and width of the blade, respectively) and incoming flow velocities was experimentally investigated in a water channel. A surface scanner was implemented to quantify bed topography, and a tomographic particle image velocimetry system was used to characterize the three-dimensional wake flows. The results showed that due to the deflection of incoming flow, the velocity magnitude increased at the lateral sides of the blade, thereby producing distinctive symmetric scour holes in these regions. The normalized morphology profiles of the sedimentary bed, which were extracted along the streamwise direction at the location of the maximum erosion depth, exhibited a self-similar pattern that closely followed a sinusoidal wave profile. The level of velocity magnitude enhancement was highly correlated to the postures of the flexible blade. At a given flow velocity, the blade with lower aspect ratios exhibited less significant deformation, causing more significant near-bed velocity enhancement in the wake deflection zone and therefore leading to higher erosion volumes. Further investigation indicated that when the blade underwent slight deformation, the larger velocity enhancement close to the bed can be attributed to more significant flow deflection effects at the lateral sides of the blade and stronger flow mixing with high momentum flows away from the bed. Supported with measurements, a basic formula was established to quantify the shear stress acting on the sedimentary bed as a function of incoming flow velocity and blade aspect ratio. 

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. 

Ookami [3] is a computer technology testbed supported by the United States National Science Foundation. It provides researchers with access to the A64FX processor developed by Fujitsu [17] in collaboration with RIKΞN [35, 37] for the Japanese path to exascale computing, as deployed in Fugaku [36], the fastest computer in the world [34]. By focusing on crucial architectural details, the ARM-based, multi-core, 512-bit SIMD-vector processor with ultrahigh-bandwidth memory promises to retain familiar and successful programming models while achieving very high performance for a wide range of applications. We review relevant technology and system details, and the main body of the paper focuses on initial experiences with the hardware and software ecosystem for micro-benchmarks, mini-apps, and full applications, and starts to answer questions about where such technologies fit into the NSF ecosystem.