https://dasya.itu.dk/tags/gpu-utilization/ Recent content in GPU utilization on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-dgxspark/ Wed, 05 Aug 2026 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/current/pito-dgxspark/ This project investigates how running multiple deep learning tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA DGX Spark. Students will deploy and benchmark various model in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between throughput, fairness, and system efficiency. This project would be suitable as a BSc or MSc thesis. The workload can be adjusted based on the thesis and group size. https://dasya.itu.dk/for-students/proposals/archive/pito-ml-orion/ Thu, 24 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-orion/ This project investigates how running multiple deep learning training tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA Jetson Orion Nano. Students will deploy and benchmark various models (e.g., CNNs, Transformers) in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between throughput, fairness, and system efficiency. This project would be suitable as a BSc or MSc thesis. https://dasya.itu.dk/for-students/proposals/archive/pito-ml-dataset/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-dataset/ This project focuses on extending an existing dataset for predicting GPU memory requirements during deep learning training by incorporating transformer-based models such as BERT, GPT, and their variants. The student will study the architecture of these models and develop training scripts to run them under controlled conditions. During training, key GPU metrics—including memory usage, utilization, and runtime characteristics—will be recorded. These observations will be used to build a richer dataset that links model specifications with their GPU resource needs. https://dasya.itu.dk/for-students/proposals/archive/pito-ml-gpu_utilization/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-gpu_utilization/ GPU offers massive computational power and parallelism through its Streaming Multiprocessors (SMs). Efficient GPU utilization is critical for maximizing performance and optimizing compute resource usage, which is measured using various metrics such as SMACT (SM Activity) and SMOCC (SM Occupancy), and DRAMA (DRAM Active). These metrics provide insight into how effectively the GPU’s SMs and memory are used during deep learning tasks. Currently, monitoring tools provide such metrics during runtime. This project aims to explore the concept of predicting GPU utilization metrics using deep learning models, trained on a large dataset of models executed on NVIDIA A100 40GB GPUs.