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The pursuit of understanding fundamental particle interactions has reached unparalleled precision levels. Particle physics detectors play a crucial role in generating low-level object signatures that encode collision physics. However, simulating these particle collisions is computational and memory intensive which will be exasperated with larger data volumes, more complex detectors, and a higher pileup environment in the High-Luminosity Large Hadron Collider. The introduction of Fast Simulation has been pivotal in overcoming computational and memory bottlenecks. The use of deep-generative models has sparked a surge of interest in surrogate modeling for detector simulations, generating particle showers that closely resemble the observed data. Nonetheless, there is a pressing need for a comprehensive evaluation of the performance of such generative models using a standardized set of metrics. In this study, we conducted a rigorous evaluation of three generative models using standard datasets and a diverse set of metrics derived from physics, computer vision, and statistics. Furthermore, we explored the impact of using full versus mixed precision modes during inference. Our evaluation revealed that the CaloDiffusion and CaloScore generative models demonstrate the most accurate simulation of particle showers, yet there remains substantial room for improvement. Our findings identified where the evaluated models fell short in accurately replicating Geant4 data. 

Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep learning. Even while several large-scale solutions offer distributed execution environments, open-source alternatives that integrate scalable runtime tools, deep learning and data frameworks on high-performance computing platforms remain crucial for accessibility and flexibility. In this paper, we introduce Deep Radical-Cylon(RC), a heterogeneous runtime system that combines data engineering, deep learning frameworks, and workflow engines across several HPC environments, including cloud and supercomputing infrastructures. Deep RC supports heterogeneous systems with accelerators, allows the usage of communication libraries like MPI, GLOO and NCCL across multi-node setups, and facilitates parallel and distributed deep learning pipelines by utilizing Radical Pilot as a task execution framework. By attaining an end-to-end pipeline including preprocessing, model training, and postprocessing with 11 neural forecasting models (PyTorch) and hydrology models (TensorFlow) under identical resource conditions, the system reduces 3.28 and 75.9 seconds, respectively. The design of Deep RC guarantees the smooth integration of scalable data frameworks, such as Cylon, with deep learning processes, exhibiting strong performance on cloud platforms and scientific HPC systems. By offering a flexible, high-performance solution for resource-intensive applications, this method closes the gap between data preprocessing, model training, and postprocessing. 

Large language models (LLMs) training is extremely data-intensive, often involving over trillion-level tokens. Although LLM datasets are usually ingested and stored in columnar formats, they often need to be converted into another format for training, which incurs significant storage and maintenance costs due to extra data copies. While eliminating the conversion would save tens of terabytes of space in costly high performance storage, this work identifies challenges that drive us to re-think the entire data pipeline. Without conversion, we find that fine-grained random access patterns incur hundreds of times efficiency drops. Specifically, the existing data pipelines have two fundamental drawbacks: (1) They cannot efficiently support directly digesting data in columnar format due to default coarse-grained I/O; (2) Solutions to the first drawback sacrifice memory footprint to cache datasets. In this paper, we present Youmu, a new data pipeline that directly feeds fine-grained columnar data into GPUs, enabling cost-efficient LLM training. Meanwhile, Youmu maintains high training accuracy, whose perplexity outperforms widely adopted local shuffle by reducing 0.3-0.7 for pretraining. Compared to performance-optimal state-of-the-art, distributed memory-based pipelines, Youmu achieves comparable throughput with 80% less memory footprint. 

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams. 

The data engineering and data science community has embraced the idea of using Python and R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these frameworks are now ever more important in order to process terabytes of data. They can easily exceed the capabilities of a single machine but also demand significant developer time and effort due to their convenience and ability to manipulate data with high-level abstractions that can be optimized. Therefore it is essential to design scalable dataframe solutions. There have been multiple efforts to be integrated into the most efficient fashion to tackle this problem, the most notable being the dataframe systems developed using distributed computing environments such as Dask and Ray. Even though Dask and Ray's distributed computing features look very promising, we perceive that the Dask Dataframes and Ray Datasets still have room for optimization In this paper, we present CylonFlow, an alternative distributed dataframe execution methodology that enables state-of-the-art performance and scalability on the same Dask and Ray infrastructure (superchargingthem!). To achieve this, we integrate ahigh-performance dataframesystem Cylon, which was originally based on an entirely different execution paradigm, into Dask and Ray. Our experiments show that on a pipeline of dataframe operators, CylonFlow achieves 30 × more distributed performance than Dask Dataframes. Interestingly, it also enables superior sequential performance due to leveraging the native C++ execution of Cylon. We believe the performance of Cylon in conjunction with CylonFlow extends beyond the data engineering domain and can be used to consolidate high-performance computing and distributed computing ecosystems. 

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need forbenchmark carpentryfor scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection calledcloudmeshwith its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).