https://dasya.itu.dk/supervisor/ehsan-yousefzadeh-asl-miandoab/ Recent content in Ehsan Yousefzadeh-Asl-Miandoab 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-nas/ Tue, 30 Sep 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-nas/ Designing neural networks that are both accurate and efficient has become an urgent challenge as the cost of deploying machine learning systems continues to grow. Traditional Neural Architecture Search (NAS) methods usually optimize for accuracy under constraints such as FLOPs, model size, or latency. However, in practice the lifecycle cost of a model is not only determined by training, but also by inference. While training is a one-off expense, inference is repeated continuously once a model is deployed, and depending on the number of queries served, it can become the dominant contributor to energy use and cost. 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-scheduling/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-scheduling/ Workload collocation has been shown as an effective method to reduce the hardware requirements for certain deep learning (DL) training tasks. On the other hand, there hasn’t been many robust open-source implementations of schedulers that incorporate workload collocation on GPUs for DL. BLOX is a framework that aims at standardizing the way we implement deep learning schedulers. In this project, our goal is to investigate ways to integrate a collocation-aware scheduler in this framework. https://dasya.itu.dk/for-students/proposals/archive/pito-ml-checkpointing/ Thu, 05 Jun 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-ml-checkpointing/ Deep learning training can run for hours or days, causing long queue times and poor quality of service (QoS) on shared clusters. Since schedulers can’t accurately predict training durations, they often wait for jobs to finish or time out, worsening the issue. Frameworks like TensorFlow and PyTorch support model checkpointing, but frequent checkpoints can slow training, while infrequent ones reduce effectiveness. Automating checkpointing could improve scheduler decisions and fairness. This project aims to (1) benchmark checkpointing overhead in TensorFlow and PyTorch, (2) identify optimal checkpointing frequencies, and (3) develop an automatic checkpointing module. 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. https://dasya.itu.dk/for-students/proposals/archive/mlforhwutilestimation/ Sun, 01 May 2022 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/mlforhwutilestimation/ Today, there are many compute- and memory-hungry data-intensive workloads from big data analytics applications to deep learning. These workloads increasingly run on shared hardware resources, which requires building hardware resource managers that can both serve the needs of workloads and utilize hardware well. Predicting the resource utilization of applications can aid such resource managers while making scheduling decisions for those applications. In this project, we aim to investigate the effectiveness of different deep learning techniques for predicting hardware resource needs of applications.