A GPU-based Correlator X-engine Implemented on the CHIME Pathfinder

被引:0
|
作者
Denman, Nolan [1 ,2 ]
Amiri, Mandana [3 ]
Bandura, Kevin [4 ]
Connor, Liam [1 ,2 ,5 ]
Dobbs, Matt [4 ,6 ]
Fandino, Mateus [3 ]
Halpern, Mark [3 ]
Hincks, Adam [3 ]
Hinshaw, Gary [3 ]
Hofer, Carolin [3 ]
Klages, Peter [1 ]
Masui, Kiyoshi [3 ,6 ]
Parra, Juan Mena [4 ]
Newburgh, Laura [1 ]
Recnik, Andre [1 ]
Shaw, J. Richard [5 ]
Sigurdson, Kris [3 ]
Smith, Kendrick [7 ]
Vanderlinde, Keith [1 ,2 ]
机构
[1] Univ Toronto, Dunlap Inst, Toronto, ON M5S 1A1, Canada
[2] Univ Toronto, Dept Astron & Astrophys, Toronto, ON M5S 1A1, Canada
[3] Univ British Columbia, Dept Phys & Astron, Vancouver, BC V5Z 1M9, Canada
[4] McGill Univ, Dept Phys, Montreal, PQ H3A 2T5, Canada
[5] Canadian Inst Theoret Astrophys, Toronto, ON, Canada
[6] Canadian Inst Adv Res, CIFAR Program Cosmol & Grav, Toronto, ON, Canada
[7] Perimeter Inst Theoret Phys, Waterloo, ON, Canada
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We present the design and implementation of a custom GPU-based compute cluster that provides the correlation X-engine of the CHIME Pathfinder radio telescope. It is among the largest such systems in operation, correlating 32,896 baselines (256 inputs) over 400MHz of radio bandwidth. Making heavy use of consumer-grade parts and a custom software stack, the system was developed at a small fraction of the cost of comparable installations. Unlike existing GPU backends, this system is built around OpenCL kernels running on consumer-level AMD GPUs, taking advantage of low-cost hardware and leveraging packed integer operations to double algorithmic efficiency. The system achieves the required 105 TOPS in a 10kW power envelope, making it one of the most power-efficient X-engines in use today.
引用
收藏
页码:35 / 40
页数:6
相关论文
共 43 条
  • [21] G-SLIDE: A GPU-Based Sub-Linear Deep Learning Engine via LSH Sparsification
    Pan, Zaifeng
    Zhang, Feng
    Li, Hourun
    Zhang, Chenyang
    Du, Xiaoyong
    Deng, Dong
    IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2022, 33 (11) : 3015 - 3027
  • [22] LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment
    Zeni, Alberto
    Guidi, Giulia
    Ellis, Marquita
    Ding, Nan
    Santambrogio, Marco D.
    Hofmeyr, Steven
    Buluc, Aydin
    Oliker, Leonid
    Yelick, Katherine
    2020 IEEE 34TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM IPDPS 2020, 2020, : 462 - 471
  • [23] Fast GPU-based spot extraction for energy-dispersive X-ray Laue diffraction
    Alghabi, F.
    Send, S.
    Schipper, U.
    Abboud, A.
    Pashniak, N.
    Pietsch, U.
    Kolb, A.
    JOURNAL OF INSTRUMENTATION, 2014, 9
  • [24] GPU-BASED VOLUME RECONSTRUCTION FROM VERY FEW ARBITRARILY ALIGNED X-RAY IMAGES
    Gross, Daniel
    Heil, Ulrich
    Schulze, Ralf
    Schoemer, Elmar
    Schwanecke, Ulrich
    SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2009, 31 (06): : 4204 - 4221
  • [25] GPU-based Segmentation of Dental X-ray Images using Active Contours Without Edges
    Ben Ali, Ramzi
    Ejbali, Ridha
    Zaied, Mourad
    2015 15TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA), 2015, : 505 - 510
  • [26] Seamless Integration of a GPU-Based Monte Carlo Treatment Plan Optimization Engine for Carbon Ion Therapy with Varian Eclipse System
    Tsai, M.
    Jia, X.
    Qin, N.
    MEDICAL PHYSICS, 2017, 44 (06)
  • [27] Application of ARCHERRT- A GPU-Based Monte Carlo Dose Engine for Radiation Therapy - to Tomotherapy and Patient-Independent IMRT
    Su, L.
    Yang, Y.
    Bednarz, B.
    Sterpin, E.
    Du, X.
    Liu, T.
    Xu, X.
    MEDICAL PHYSICS, 2014, 41 (06)
  • [28] Rapid GPU-based simulation of X-ray transmission, scatter, and phase measurements for threat detection systems
    Gong, Qian
    Coccarelli, David
    Stoian, Razvan-Ionut
    Greenberg, Joel
    Vera, Esteban
    Gehm, Michael
    ANOMALY DETECTION AND IMAGING WITH X-RAYS (ADIX), 2016, 9847
  • [29] A Novel Breast Phantom Created Using GPU-Based Voxelization for X-Ray Breast Imaging Research
    Bliznakova, Kristina
    Bliznakov, Zhivko
    2024 12TH E-HEALTH AND BIOENGINEERING CONFERENCE, EHB 2024, 2024, : 285 - 288
  • [30] Fast GPU-based absolute intensity determination for energy-dispersive X-ray Laue diffraction
    Alghabi, F.
    Send, S.
    Schipper, U.
    Abboud, A.
    Pietsch, U.
    Kolb, A.
    JOURNAL OF INSTRUMENTATION, 2016, 11