https://dasya.itu.dk/tags/hardware-resource-consumption/ Recent content in hardware resource consumption on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Tue, 20 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/workloadcharacterization/ Tue, 20 Apr 2021 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/workloadcharacterization/ A data science infrastructure orchestrates the execution of widely used machine learning frameworks (e.g., TensorFlow , PyTorch) on a heterogeneous set of processing units (e.g., CPU, GPU, TPU, FPGA) while powering an increasingly diverse and complex range of applications (e.g., fraud detection, healthcare, virtual assistance, automatic driving). Understanding the resource consumption characteristics of different data science workloads is a necessary step before designing effective hardware resource management in any data science infrastructure.