Deep learning is an area emerged by the capabilities of machine learning and artificial intelligence. In the field ofdeep learning, various parameters are essen

The search efficiency of in-memory databases depends significantly on how quickly DRAM data can be obtained. With an increasing multiple core on processors, it is more difficult to satisfy the requirement that all attached DRAM on a processor is equally accessible to any core. Intel has therefore implemented a Sub-NUMA Clustering (SNC) mode on Skylake, which subdivides cores and memories into various sub for enhanced core-to-memory access within each processor sub-domain. Similar modes are given by other models. When an in-memory database shares data between staff on cores in separate sub-domains, the use of SNC creates problems in how to manage database workloads among domains. In this research, we verify the effect of SNC specifically on Intel Skylake on memory latency and bandwidth. We conclude that two similarly broad analytical workloads are focused on different and fully independent sub-domains just up to 3 percent will improve query throughput and be totally isolated from each other. Often, as memory bandwidth is split into sub-domains rather than aggregated across the entire processor, bandwidth-sensitive analytical workloads significantly reduce application performance if data is not split equally among sub-domains.

Background/Objectives: Cardiovascular diseases (CVDs) represent the foremost public health challenge of recent days. According to the World Health Organization (WHO), nearly 32% of people die from CVDs each year. This paper introduces a quantum-inspired, information-theoretic framework for comparing CVD risk-factor architectures in modeled Indian and global populations. Methods: Each synthetic population is represented as a population state matrix ($\rho$) built from discretized epidemiological variables (age, sex, smoking, diabetes, hypertension, and dyslipidemia). Systemic uncertainty is quantified with von Neumann entropy (equivalent to Shannon entropy for diagonal states), inter-factor dependence with Systemic Mutual Information (SMI), and synthetic population similarity with Uhlmann fidelity. Results: In illustrative modeled scenarios ($N=10,000$ synthetic profiles per synthetic population), diabetes entropy is higher in the Indian comparator ($0.88$ vs. $0.61$ bits), diabetes–hypertension SMI is larger in India (≈0.46 vs. $0.22$ bits), and smoking–hypertension dependence is stronger globally ($0.36$ vs. $0.21$ bits). Overall fidelity is $F\approx 0.76$. Conclusions: These results are hypothesis-generating point estimates and require clinical data verification on harmonized synthetic individual-level data before clinical or policy use. This study represents a quantum information-theoretic framework for comparing modeled CVD risk profiles. Illustrative results suggest stronger diabetes–hypertension dependence in the Indian scenario and stronger smoking–hypertension dependence in the global comparator.