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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.

 

The engineering samples of the NVIDIA Grace CPU Superchip and NVIDIA Grace Hopper Superchips were tested using different benchmarks and scientific applications. The benchmarks include HPCC and HPCG. The real application-based benchmark includes AI-Benchmark-Alpha (a TensorFlow benchmark), Gromacs, OpenFOAM, and ROMS. The performance was compared to multiple Intel, AMD, ARM CPUs and several x86 with NVIDIA GPU systems. A brief energy efficiency estimate was performed based on TDP values. We found that in HPCC benchmark tests, the per-core performance of Grace is similar to or faster than AMD Milan cores, and the high core count often allows NVIDIA Grace CPU Superchip to have per-node performance similar to Intel Sapphire Rapids with High Bandwidth Memory: slower in matrix multiplication (by 17%) and FFT (by 6%), faster in Linpack (by 9%)). In scientific applications, the NVIDIA Grace CPU Superchip performance is slower by 6% to 18% in Gromacs, faster by 7% in OpenFOAM, and right between HBM and DDR modes of Intel Sapphire Rapids in ROMS. The combined CPU-GPU performance in Gromacs is significantly faster (by 20% to 117% faster) than any tested x86-NVIDIA GPU system. Overall, the new NVIDIA Grace Hopper Superchip and NVIDIA Grace CPU Superchip Superchip are high-performance and most likely energy-efficient solutions for HPC centers. 

Patterns in foliar nitrogen (N) stable isotope ratios (δ15N) have been shown to reveal trends in terrestrial N cycles, including the identification of ecosystems where N deficiencies limit forest ecosystem productivity. However, there is a gap in our understanding of within-species variation and species-level response to environmental gradients or forest management. Our objective is to examine the relationship between site index, foliar %N, foliar δ15N and spectral reflectance for managed Douglas-fir (Pseudotsuga menziesii) and loblolly pine (Pinus taeda) plantations across their geographic ranges in the Pacific Northwest and the southeastern United States, respectively. Foliage was measured at 28 sites for reflectance using a handheld spectroradiometer, and further analyzed for δ15N and N concentration. Unlike the prior work for grasslands and shrubland species, our results show that foliar δ15N and foliar %N are not well correlated for these tree species. However, multiple linear regression models suggest a strong predictive ability of spectroscopy data to quantify foliar δ15N, with some models explaining more than 65% of the variance in the δ15N. Additionally, moderate to strong explanations of variance were found between site index and foliar δ15N (R2 = 0.49) and reflectance and site index (R2 = 0.84) in the Douglas-fir data set. The development of relationships between foliar spectral reflectance, δ15N and measures of site productivity provides the first step toward mapping canopy δ15N for these managed forests with remote sensing. 

We present and evaluate TTG, a novel programming model and its C++ implementation that by marrying the ideas of control and data flowgraph programming supports compact specification and efficient distributed execution of dynamic and irregular applications. Programming interfaces that support task-based execution often only support shared memory parallel environments; a few support distributed memory environments, either by discovering the entire DAG of tasks on all processes, or by introducing explicit communications. The first approach limits scalability, while the second increases the complexity of programming. We demonstrate how TTG can address these issues without sacrificing scalability or programmability by providing higher-level abstractions than conventionally provided by task-centric programming systems, without impeding the ability of these runtimes to manage task creation and execution as well as data and resource management efficiently. TTG supports distributed memory execution over 2 different task runtimes, PaRSEC and MADNESS. Performance of four paradigmatic applications (in graph analytics, dense and block-sparse linear algebra, and numerical integrodifferential calculus) with various degrees of irregularity implemented in TTG is illustrated on large distributed-memory platforms and compared to the state-of-the-art implementations.