Sökning: "Tensor Core"

Visar resultat 1 - 5 av 6 uppsatser innehållade orden Tensor Core.

1. 1. Accelerating a Molecular Docking Application by Leveraging Modern Heterogeneous Computing Systems

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gabin Schieffer; [2023]
   Nyckelord :Molecular docking; AutoDock; GPU; Tensor Core; Drug Discovery; Molekylär dockning; AutoDock; GPU; Tensor Core; Läkemedelsutveckling;

   Sammanfattning : In drug development, molecular docking methods aim at characterizing the binding of a drug-like molecule to a protein. In a typical drug development process, a docking task is repeated millions of time, which makes optimization efforts essential. LÄS MER

3. 2. Register Caching for Energy Efficient GPGPU Tensor Core Computing

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Qiran Qian; [2023]
   Nyckelord :Computer Architecture; GPGPU; Tensor Core; GEMM; Energy Efficiency; Register File; Cache; Instruction Scheduling; Datorarkitektur; GPGPU; Tensor Core; GEMM; energieffektivitet; registerfil; cache; instruktionsschemaläggning;

   Sammanfattning : The General-Purpose GPU (GPGPU) has emerged as the predominant computing device for extensive parallel workloads in the fields of Artificial Intelligence (AI) and Scientific Computing, primarily owing to its adoption of the Single Instruction Multiple Thread architecture, which not only provides a wealth of thread context but also effectively hide the latencies exposed in the single threads executions. As computational demands have evolved, modern GPGPUs have incorporated specialized matrix engines, e. LÄS MER

4. 3. Accelerating bulk material property prediction using machine learning potentials for molecular dynamics : predicting physical properties of bulk Aluminium and Silicon

   Master-uppsats, Linköpings universitet/Teoretisk Fysik

   Författare :Nicholas Sepp Löfgren; [2021]
   Nyckelord :machine learning; moment tensor potential; kernel ridge regression; molecular dynamics; density functional theory; materials science; data-driven materials design; maskininlärning; molekylärdynamik; täthetsfunktionalteori; materialvetenskap; datadriven materialdesign;

   Sammanfattning : In this project machine learning (ML) interatomic potentials are trained and used in molecular dynamics (MD) simulations to predict the physical properties of total energy, mean squared displacement (MSD) and specific heat capacity for systems of bulk Aluminium and Silicon. The interatomic potentials investigated are potentials trained using the ML models kernel ridge regression (KRR) and moment tensor potentials (MTPs). LÄS MER

5. 4. Speeding up PARAFAC : Approximation of tensor rank using the Tucker core

   Master-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Lukas Arnroth; [2018]
   Nyckelord :Tucker decomposition; PARAFAC; tensor rank; split-half analysis;

   Sammanfattning : In this paper, the approach of utilizing the core tensor from the Tucker decomposition, in place of theuncompressed tensor, for nding a valid tensor rank for the PARAFAC decomposition is considered.Validity of the proposed method is investigated in terms of error and time consumption. LÄS MER

7. 5. An Evaluation of TensorFlow as a Programming Framework for HPC Applications

   Master-uppsats, KTH/Beräkningsvetenskap och beräkningsteknik (CST); KTH/Parallelldatorcentrum, PDC

   Författare :Wei Der Chien; [2018]
   Nyckelord :HPC; GPU; TensorFlow;

   Sammanfattning : In recent years, deep-learning, a branch of machine learning gained increasing popularity due to their extensive applications and performance. At the core of these application is dense matrix-matrix multiplication. Graphics Processing Units (GPUs) are commonly used in the training process due to their massively parallel computation capabilities. LÄS MER