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As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent's unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for…
Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade. This expansion has relied on sustained efforts by large technology corporations to aggregate and curate internet-scale datasets.…
The surge for computing resource demand is increasing global electricity consumption in data centers which is expected to exceed 1000 TWh by 2026, mainly attributable to adoption of new AI technologies. Carbon-aware computing strategies can…
Large language models (LLMs) are increasingly used for analytical tasks, yet their effectiveness in real-world applications remains underexamined, partly due to the opacity of proprietary models. We evaluate ChatGPT (GPT-3.5 and GPT-4) on…
One of the current trends related to data centers is providing it with renewable energy sources. This paper suggests an analysis technique for a model uses solar panels energy to power a data center consists of 100 traditional servers,…
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Large Language Models (LLMs) like GPT-4 and LLaMA have shown incredible proficiency at natural language processing tasks and have even begun to excel at tasks across other modalities such as vision and audio. Despite their success, LLMs…
Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand. Many systems run inefficiently due to planning flaws, operational faults, or…
Global data assimilation enables weather forecasting at all scales and provides valuable data for studying the Earth system. However, the computational demands of physics-based algorithms used in operational systems limits the volume and…
Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM)…
With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power…
Large language models (LLMs) are used in software development to assist in various tasks, e.g., code generation and code completion, but empirical evaluations of the quality of the results produced by these models focus on correctness and…
Large Language Models (LLMs) are widely used for code generation. However, commercial models like ChatGPT require significant computing power, which leads to high energy use and carbon emissions. This has raised concerns about their…
Sensitivity analysis is a cornerstone of climate science, essential for understanding phenomena ranging from storm intensity to long-term climate feedbacks. However, computing these sensitivities using traditional physical models is often…
The rapid growth of data volume brings big challenges to the data center computing, and energy efficiency is one of the most concerned problems. Researchers from various fields are now proposing solutions to green the data center…
Most FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization of their network capabilities is often challenging and…
The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP…
Information technology organizations and companies are seeking greener alternatives to traditional terrestrial data centers to mitigate global warming and reduce carbon emissions. Currently, terrestrial data centers consume a significant…
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We introduce EfficientLLM, a novel benchmark and the first…
Discovering insights from a real-world data lake potentially containing unclean, semi-structured, and unstructured data requires a variety of data processing tasks, ranging from extraction and cleaning to integration, analysis, and…