Cholesky factorization is a core operation in scientific computing, yet its scalability is often constrained by memory limitations when processing extremely large dense matrices. This work introduces an out-of-core Cholesky factorization algorithm for symmetric positive-definite matrices that integrates GPU acceleration, block-wise lossless compression, and parallel I/O to overcome these limitations. The approach leverages the OMPC runtime for asynchronous task scheduling and employs HDF5 to store the matrix on disk, taking advantage of Lustre's parallel I/O capabilities in distributed environments. Tiles are decompressed just-in-time on the GPU, significantly reducing host memory usage, storage footprint, and end-to-end data movement overhead-from disk through the CPU to the GPU-without compromising numerical accuracy. Experimental results show that the proposed method scales across 8 GPU nodes, successfully factorizing matrices up to 3M x 3M. In comparison, SLATE could only handle sizes up to 700K x 700K, with the proposed algorithm achieving up to 41% higher throughput. These results demonstrate the algorithm's scalability and competitiveness beyond memory-constrained in-core solutions, offering a practical path for enabling extreme-scale scientific applications.
The rapid scaling of deep learning (DL) models raises the need for accurate and understandable performance/energy prediction tools to support efficient resource management and sustainable AI development. Existing modeling approaches often lack both sufficient granularity to capture nuanced hardware-software interactions and suitable flexibility to adapt to diverse modern architectures. This paper introduces an analytical framework for time/energy prediction of DL training workloads on GPU. Our framework integrates detailed workload characterization that includes FLOPs, memory access, kernel activities, and novel structural features to derive an architecture-aware efficiency model, which considers a saturation-based function to capture dimensional scaling effects on hardware utilization. We propose an iterative refinement methodology, which incorporates model-specific scalars to address particular architectures like ALBERT and precision-specific calibrations for BF16 operations. Our benchmark with six advanced DL models (including CNNs, BERT-style Transformers, and LLMs like TinyLlama) on NVIDIA A100 GPUs under various configurations (1/4 GPUs, FP32/TF32/mixed BF16) shows that our approach achieves a high predictive accuracy, with an overall relative error of 4.14% (3.05% for time, 5.78% for power). The framework is intended to provide valuable insights for HPC-AI co-design, energy-aware scheduling, and performance optimization.
Training in supervised machine learning is based on the availability of datasets; however, medical datasets must comply with stringent privacy regulations. Generative Adversarial Networks (GANs) are a relevant alternative to solve the limitation of small medical datasets due to their ability to generate additional data with desired features. A significant drawback of these models is that they may produce unrealistic, blurred, or insufficiently diverse images. This paper proposes a data augmentation technique using GANs to create synthetic Magnetic Resonance Imaging (MRI) of four stages of Alzheimer's Disease (AD): non-demented, very mild demented, mild demented, and moderate demented. We designed a GAN based on the Pix2Pix model, which learns the features of each AD stage. Generated images are evaluated by multistage Convolutional Neural Network (CNN) models, greyscale histograms of the distribution of pixel intensities, and brain mass measurements on binarized images. The results indicate that AD synthetic MRI effectively captures disease patterns, demonstrating the potential of GANs to improve training and diagnosis of neurodegenerative diseases.
Recently, Graph Neural Networks (GNN) have been integrated into various local applications, such as local community detection and local code assistant, making edge inference increasingly important. To support diverse workloads, state-of-the-art edge devices have evolved into heterogeneous platforms, integrating components like CPU, GPU, and NPU. To this end, we propose GNX, a novel GNN system that accelerates GNN inference on edge heterogeneous platforms by leveraging all the heterogeneous processing units. Given a GNN model and a heterogeneous platform, GNX automatically generates parallel execution plans, consisting of both data and pipeline parallelism. To reduce the complexity of the design space, GNX converts GNN models into coarse-grained blocks and performs the search at the block level. By leveraging the APIs provided by state-of-the-art heterogeneous frameworks, GNX can flexibly schedule various parallel execution plans and seamlessly adjust the workload across the heterogeneous processing units for load-balanced execution. Our study shows that GNX effectively accelerates three widely-used GNN models on two state-of-the-art edge heterogeneous platforms. Compared with the baseline approach that uses only a single processing unit, GNX achieves up to a 2.57x speedup. Compared with adopting data parallelism and a state-of-the-art scheduler, GNX achieves up to 1.90x and 1.79x speedup, respectively. We also discuss the applicability of and extensions to GNX to support other GNN models.
Computing continuum systems are emerging as a solution for organizations to process data across diverse infrastructures, reducing latency compared to traditional cloud computing. However, managing I/O operations in such distributed and heterogeneous environments remains an open research challenge. In this paper, we present A-Flow, a model for constructing I/O systems to manage data exchange in computing continuum environments. These systems are built around data distribution patterns defined by structures called abstract communication channels (ACCs). ACCs are established between processing stages using memory, file system, and network resource connections. To prevent resource overload during execution, A-Flow automatically selects the appropriate communication channel based on user-defined criteria such as throughput or resource utilization. We implemented this model in a prototype and evaluated it through a case study focused on managing medical data in HDF5 format across different environments. The evaluation revealed that A-Flow’s ACCs can be integrated into existing stage-based systems found in the state of the art. The results highlight the efficiency and effectiveness of A-Flow in enabling dataflows across heterogeneous infrastructures, addressing key challenges in computing continuum.
