Related papers: Performance Antipatterns: Angel or Devil for Power…
Processor design validation and debug is a difficult and complex task, which consumes the lion's share of the design process. Design bugs that affect processor performance rather than its functionality are especially difficult to catch,…
Several companies are re-architecting their monolithic information systems with microservices. However, many companies migrated without experience on microservices, mainly learning how to migrate from books or from practitioners' blogs.…
The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on the consumption…
This paper presents refinements to the execution-cache-memory performance model and a previously published power model for multicore processors. The combination of both enables a very accurate prediction of performance and energy…
The energy consumption of an exascale High-Performance Computing (HPC) supercomputer rivals that of tens of thousands of people in terms of electricity demand. Given the substantial energy footprint of exascale HPC systems and the…
As AI's energy demand continues to grow, it is critical to enhance the understanding of characteristics of this demand, to improve grid infrastructure planning and environmental assessment. By combining empirical measurements from…
The rise of IoT has increased the need for on-edge machine learning, with TinyML emerging as a promising solution for resource-constrained devices such as MCU. However, evaluating their performance remains challenging due to diverse…
The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increase in data center energy consumption. To improve energy…
Software engineers make use of design patterns for reasons that range from performance to code comprehensibility. Several design patterns capturing the body of knowledge of best practices have been proposed in the past, namely creational,…
In the current high-performance and embedded computing era, full-stack energy-centric design is paramount. Use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Extreme…
With heterogeneous systems, the number of GPUs per chip increases to provide computational capabilities for solving science at a nanoscopic scale. However, low utilization for single GPUs defies the need to invest more money for expensive…
Power and energy consumption is becoming key challenges to deploy the first exascale supercomputer successfully. Large-scale HPC applications waste a significant amount of power in communication and synchronization-related idle times.…
Recent decades have witnessed a surge in the development of concurrent data structures with an increasing interest in data structures implementing concurrent sets (CSets). Microbenchmarking tools are frequently utilized to evaluate and…
Nowadays, improving the energy efficiency of high-performance computing (HPC) systems is one of the main drivers in scientific and technological research. As large-scale HPC systems require some fault-tolerant method, the opportunities to…
Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing…
The ubiquity of machine learning (ML) and the demand for ever-larger models bring an increase in energy consumption and environmental impact. However, little is known about the energy scaling laws in ML, and existing research focuses on…
In the face of surging power demands for exascale HPC systems, this work tackles the critical challenge of understanding the impact of software-driven power management techniques like Dynamic Voltage and Frequency Scaling (DVFS) and Power…
Context: Along with developing Deep learning (DL) models, larger datasets and more complex model structures are applied, leading to rising computing resources and energy consumption, which is an alert that green DL models should receive…
Microservices architecture has started a new trend for application development for a number of reasons: (1) to reduce complexity by using tiny services; (2) to scale, remove and deploy parts of the system easily; (3) to improve flexibility…
Sustainability reporting for web-based services often relies on simplified end-user energy models that assume constant laptop power during browser interactions. Energy models such as Digst and DIMPACT apply fixed power values (15-22W), yet…