Nvidia is a leading producer of GPUs for high-performance computing and artificial intelligence, bringing top performance and energy-efficiency. We present performance, power consumption, and thermal behavior analysis of the new Nvidia DGX-A100 server equipped with eight A100 Ampere microarchitecture GPUs. The results are compared against the previous generation of the server, Nvidia DGX-2, based on Tesla V100 GPUs. We developed a synthetic benchmark to measure the raw performance of floating-point computing units including Tensor Cores. Furthermore, thermal stability was investigated. In addition, Dynamic Frequency and Voltage Scaling (DVFS) analysis was performed to determine the best energy-efficient configuration of the GPUs executing workloads of various arithmetical intensities. Under the energy-optimal configuration the A100 GPU reaches efficiency of 51 GFLOPS/W for double-precision workload and 91 GFLOPS/W for tensor core double precision workload, which makes the A100 the most energy-efficient server accelerator for scientific simulations in the market.

Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today’s clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union’s Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. From the HPC domain, the Periscope Tuning Framework (PTF) is extended to perform dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach.

This paper describes the Hybrid Total FETI (HTFETI) method and its parallel implementation in the ESPRESO library. HTFETI is a variant of the FETI type domain decomposition method in which a small number of neighboring subdomains is aggregated into clusters. This can be also viewed as a multilevel decomposition approach which results into a smaller coarse problem - the main scalability bottleneck of the FETI and FETI-DP methods. The efficiency of our implementation which employs hybrid parallelization in the form of MPI and Cilk++ is evaluated using both weak and strong scalability tests. The weak scalability of the solver is shown on the 3 dimensional linear elasticity problem of a size up to 30 billion of Degrees Of Freedom (DOF) executed on 4096 compute nodes. The strong scalability is evaluated on the problem of size 2.6 billion DOF scaled from 1000 to 4913 compute nodes. The results show the super-linear scaling of the single iteration time and linear scalability of the solver runtime. The latter combines both numerical and parallel scalability and shows overall HTFETI solver performance. The large scale tests use our own parallel synthetics benchmark generator that is also described in the paper. The last set of results shows that HTFETI is very efficient for problems of size up 1.7 billion DOF and provide better time to solution when compared to TFETI method.

Background and motivation: Liver segmentation plays a critical role in medical imaging, aiding in diagnosis, treatment planning, and surgical interventions for liver diseases. Precise segmentation of liver structures, including vessels, tumors, and other substructures, is essential for effective patient management. Traditional manual methods are time-consuming and prone to variability, prompting the development of automated techniques. This review aims to evaluate the evolution of liver segmentation methodologies, focusing on recent advancements in deep learning and hybrid approaches. Materials and methods: This review follows the PRISMA guidelines for systematic analysis, including a detailed database search across PubMed, Web of Science, Scopus, and IEEE Xplore. The search focused on segmentation techniques for various liver structures using deep learning, traditional methods, and hybrid models. A total of 7819 studies were initially identified, with 190 selected for detailed analysis based on inclusion criteria like Dice Similarity Coefficient (DSC) metrics and clinical applicability. Results: The analysis identified deep learning models, such as U-Net variants and Swin Transformer-based architectures, as leading methods for liver parenchyma and tumor segmentation, achieving DSC values up to 98.9% on benchmark datasets. For vessel segmentation, methods like DeepLabV3+ and the feature-based approaches demonstrated robustness across different datasets. Despite progress, challenges remain in segmenting structures like biliary ducts and hematomas due to limited annotated data and imaging variability. Discussion: While deep learning has significantly improved segmentation accuracy, challenges such as class imbalance and variability across imaging modalities persist. Hybrid approaches that combine traditional image processing with advanced neural networks show potential for further improvement. Future research should focus on enhancing generalizability through multi-modal data integration and exploring semi-supervised learning methods to overcome data scarcity. Conclusion: This comprehensive review highlights the advancements and ongoing challenges in liver segmentation, emphasizing the need for continuous innovation. By addressing current limitations, future methodologies can improve accuracy, efficiency, and clinical relevance, ultimately enhancing patient outcomes in hepatology.