PDFFlow: hardware accelerating parton density access

• URL: http://arxiv.org/abs/2012.08221v1
• Date: Tue, 15 Dec 2020 11:22:12 GMT
• Title: PDFFlow: hardware accelerating parton density access
• Authors: Marco Rossi, Stefano Carrazza, Juan M. Cruz-Martinez
• Abstract summary: We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) PDFFlow is designed for platforms with hardware accelerators. We benchmark the performance of this library on multiple scenarios for the particle physics community.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluons at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the possibility to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community.

Related papers

• PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling [63.93112754821312]
  Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. We introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents.
  arXiv  Detail & Related papers  (2024-10-08T12:17:42Z)
• PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator [73.80050807279461]
  Piecewise Rectified Flow (PeRFlow) is a flow-based method for accelerating diffusion models. PeRFlow achieves superior performance in a few-step generation.
  arXiv  Detail & Related papers  (2024-05-13T07:10:53Z)
• Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling [49.215957313126324]
  Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling. However, IRN-based methods tend to produce over-smoothed results, while GAN-based methods easily generate fake details. We propose Boundary-aware Decoupled Flow Networks (BDFlow) to generate realistic and visually pleasing results.
  arXiv  Detail & Related papers  (2024-05-05T14:05:33Z)
• Towards a high-performance AI compiler with upstream MLIR [34.89141656581549]
  This work proposes a compilation flow using open-source compiler passes to build a framework to achieve ninja performance. We demonstrate this flow with a proof-of-concept MLIR project that uses input IR in Linalg-on-Tensor from Packing and PyTorch.
  arXiv  Detail & Related papers  (2024-04-15T10:35:50Z)
• Dynamic Frame Interpolation in Wavelet Domain [57.25341639095404]
  Video frame is an important low-level computation vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts.
  arXiv  Detail & Related papers  (2023-09-07T06:41:15Z)
• PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks [68.96484488899901]
  We present PARTIME, a library designed to speed up neural networks whenever data is continuously streamed over time. PARTIME starts processing each data sample at the time in which it becomes available from the stream. Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning.
  arXiv  Detail & Related papers  (2022-10-17T14:49:14Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest [0.0]
  We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning algorithm. We use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under $6$ min with consumer computer hardware.
  arXiv  Detail & Related papers  (2020-12-10T19:00:15Z)
• PDFFlow: parton distribution functions on GPU [1.2891210250935146]
  We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) PDFFlow is designed for platforms with hardware accelerators. We benchmark the performance of this library on multiple scenarios for the particle physics community.
  arXiv  Detail & Related papers  (2020-09-14T18:00:00Z)
• An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure [0.3277163122167433]
  We introduce an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks. The algorithm is validated for the so-called DLR-A turbulent jet diffusion flame using both RANS and LES simulations.
  arXiv  Detail & Related papers  (2020-05-18T00:13:55Z)
• VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms [0.0]
  We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration techniques. This software is inspired on the Vegas algorithm, ubiquitous in the particle physics community as the driver cross section integration, and based on Google's powerful library.
  arXiv  Detail & Related papers  (2020-02-28T18:48:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.