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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Enhanced data security, better data integrity, and efficient access to information. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.

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What is Cloud Computing? According to ChatGPT.

High Scalability

This model of computing has become increasingly popular in recent years, as it offers a number of benefits, including cost savings, flexibility, scalability, and increased efficiency. This means that users only pay for the computing resources they actually use, rather than having to invest in expensive hardware and software upfront.

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What is cloud migration?

Dynatrace

Cloud migration is the process of transferring some or all your data, software, and operations to a cloud-based computing environment that offers unlimited scale and high availability. This can fundamentally transform how they work, make processes more efficient, and improve the overall customer experience. Here are three.

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Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

ACM Sigarch

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. Jason Lowe-Power (UC Davis) discussed smart memory management and the need for an efficient interface for it.

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Cloud Native Predictions for 2024

Percona

All of the database automation for running a highly available, resilient, and secure database is built into the operator to simplify the operation and management of your clusters. Consequently, they might miss out on the benefits of integrating security into the SDLC, such as enhanced efficiency, speed, and quality in software delivery.

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A Brief Guide of xPU for AI Accelerators

ACM Sigarch

GPU: Graphics Processing Unit (GPU) , which achieves high data parallelism with its SIMD architecture, has played a great role in the current AI market, from training to inference. HPU: Holographic Processing Unit (HPU) is the specific hardware of Microsoft’s Hololens. FPU: Floating Processing Unit (FPU). The new GV100 packs 7.4

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A case for managed and model-less inference serving

The Morning Paper

As we saw with the SOAP paper last time out, even with a fixed model variant and hardware there are a lot of different ways to map a training workload over the available hardware. Different hardware architectures (CPUs, GPUs, TPUs, FPGAs, ASICs, …) offer different performance and cost trade-offs.