Optimizing Server Efficiency and Resource Management

Current Trends in Server Efficiency and Resource Management

Recent developments in the research area of server efficiency and resource management have shown a strong focus on optimizing performance, reducing energy consumption, and enhancing the adaptability of systems to varying workloads. The field is moving towards more intelligent and automated solutions that leverage machine learning and advanced profiling techniques to dynamically allocate resources and predict performance outcomes. This shift is driven by the need for more sustainable and cost-effective computing solutions, particularly in cloud and edge environments.

One of the key innovations is the integration of input-aware memory allocation in serverless computing functions, which significantly reduces resource wastage and operational costs. Additionally, the use of large language models to optimize software for energy efficiency and the development of specialized compilers for processing-in-memory accelerators are pushing the boundaries of what is possible in terms of performance and energy savings.

Notable advancements include:

  • Input-Based Ensemble-Learning Method for Dynamic Memory Configuration: This approach significantly reduces resource allocation and runtime costs by up to 87%.
  • PIMCOMP: An End-to-End DNN Compiler for Processing-In-Memory Accelerators: This compiler improves throughput, latency, and energy efficiency across various architectures.
  • FluidML: Fast and Memory Efficient Inference Optimization: This framework reduces inference latency by up to 25.38% and peak memory usage by up to 41.47%.

Sources

Overhead Measurement Noise in Different Runtime Environments

16 Years of SPEC Power: An Analysis of x86 Energy Efficiency Trends

Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions

OSCAR-P and aMLLibrary: Profiling and Predicting the Performance of FaaS-based Applications in Computing Continua

Interoperability From Kieker to OpenTelemetry: Demonstrated as Export to ExplorViz

Achieving Consistent and Comparable CPU Evaluation Outcomes

Can Large-Language Models Help us Better Understand and Teach the Development of Energy-Efficient Software?

PIMCOMP: An End-to-End DNN Compiler for Processing-In-Memory Accelerators

FluidML: Fast and Memory Efficient Inference Optimization

Sustainable Hardware Specialization

An Optimizing Just-In-Time Compiler for Rotor

OpenGeMM: A High-Utilization GeMM Accelerator Generator with Lightweight RISC-V Control and Tight Memory Coupling

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