Implementation of the conjugate gradient algorithm on FPGA devices

Results of porting parts of the Lattice Quantum Chromodynamics code to modern FPGA devices are presented. A single-node, double precision implementation of the Conjugate Gradient algorithm is used to invert numerically the Dirac-Wilson operator on a 4-dimensional grid on a Xilinx Zynq evaluation board. The code is divided into two software/hardware parts in such a way that the entire multiplication by the Dirac operator is performed in programmable logic, and the rest of the algorithm runs on the ARM cores. Optimized data blocks are used to efficiently use data movement infrastructure allowing to reach intervals of 1 clock cycle. We show that the FPGA implementation can offer a comparable performance compared to that obtained using Intel Xeon Phi KNL.Comment: Proceedings of the 36th Annual International Symposium on Lattice Field Theory - LATTICE201

Towards Lattice Quantum Chromodynamics on FPGA devices

In this paper we describe a single-node, double precision Field Programmable Gate Array (FPGA) implementation of the Conjugate Gradient algorithm in the context of Lattice Quantum Chromodynamics. As a benchmark of our proposal we invert numerically the Dirac-Wilson operator on a 4-dimensional grid on three Xilinx hardware solutions: Zynq Ultrascale+ evaluation board, the Alveo U250 accelerator and the largest device available on the market, the VU13P device. In our implementation we separate software/hardware parts in such a way that the entire multiplication by the Dirac operator is performed in hardware, and the rest of the algorithm runs on the host. We find out that the FPGA implementation can offer a performance comparable with that obtained using current CPU or Intel's many core Xeon Phi accelerators. A possible multiple node FPGA-based system is discussed and we argue that power-efficient High Performance Computing (HPC) systems can be implemented using FPGA devices only.Comment: 17 pages, 4 figure

Implementation of the conjugate gradient algorithm in Lattice QCD on FPGA devices

Results of porting parts of the Lattice Quantum Chromodynamics code to modern FPGA devices are presented. A single-node, double precision implementation of the Conjugate Gradient algorithm is used to invert numerically the Dirac-Wilson operator on a 4-dimensional grid on a Xilinx Zynq evaluation board. The code is divided into two software/hardware parts in such a way that the entire multiplication by the Dirac operator is performed in programmable logic, and the rest of the algorithm runs on the ARM cores. Optimized data blocks are used to efficiently use data movement infrastructure allowing to reach intervals of 1 clock cycle. We show that the FPGA implementation can offer a comparable performance compared to that obtained using Intel Xeon Phi KN

J-PET Framework: Software platform for PET tomography data reconstruction and analysis

J-PET Framework is an open-source software platform for data analysis, written in C++ and based on the ROOT package. It provides a common environment for implementation of reconstruction, calibration and filtering procedures, as well as for user-level analyses of Positron Emission Tomography data. The library contains a set of building blocks that can be combined by users with even little programming experience, into chains of processing tasks through a convenient, simple and well-documented API. The generic input-output interface allows processing the data from various sources: low-level data from the tomography acquisition system or from diagnostic setups such as digital oscilloscopes, as well as high-level tomography structures e.g. sinograms or a list of lines-of-response. Moreover, the environment can be interfaced with Monte Carlo simulation packages such as GEANT and GATE, which are commonly used in the medical scientific community.Comment: 14 pages, 5 figure

Implementation of the conjugate gradient algorithm for heterogeneous systems

Lattice QCD calculations require significant computational effort, with the dominant fraction of resources typically spent in the numerical inversion of the Dirac operator. One of the simplest methods to solve such large and sparse linear systems is the conjugate gradient (CG) approach. In this work we present an implementation of CG that can be executed on different devices, including CPUs, GPUs, and FPGAs. This is achieved by using the SYCL/DPC++ framework, which allows the execution of the same source code on heterogeneous systems

Implementation of the conjugate gradient algorithm for heterogeneous systems

Lattice QCD calculations require significant computational effort, with the dominant fraction of resources typically spent in the numerical inversion of the Dirac operator. One of the simplest methods to solve such large and sparse linear systems is the conjugate gradient (CG) approach. In this work we present an implementation of CG that can be executed on different devices, including CPUs, GPUs, and FPGAs. This is achieved by using the SYCL/DPC++ framework, which allows the execution of the same source code on heterogeneous systems

TOFtracker: combination of time-of-flight and high-accuracy bidimensional tracking in a single gaseous detector

A 5-gap timing RPC equipped with patterned electrodes coupled to both charge-sensitive and timing circuits yields a time accuracy of 77 ps along with a position accuracy of 38 μm. These results were obtained by calculating the straight-line fit residuals to the positions provided by a 3-layer telescope made out of identical detectors, detecting almost perpendicular cosmic-ray muons. The device may be useful for particle identification by time-of-flight, where simultaneous measurements of trajectory and time are necessary

3D PET image reconstruction based on Maximum Likelihood Estimation Method (MLEM) algorithm

Positron emission tomographs (PET) do not measure an image directly. Instead, they measure at the boundary of the field-of-view (FOV) of PET tomograph a sinogram that consists of measurements of the sums of all the counts along the lines connecting two detectors. As there is a multitude of detectors build-in typical PET tomograph structure, there are many possible detector pairs that pertain to the measurement. The problem is how to turn this measurement into an image (this is called imaging). Decisive improvement in PET image quality was reached with the introduction of iterative reconstruction techniques. This stage was reached already twenty years ago (with the advent of new powerful computing processors). However, three dimensional (3D) imaging remains still a challenge. The purpose of the image reconstruction algorithm is to process this imperfect count data for a large number (many millions) of lines-of-responce (LOR) and millions of detected photons to produce an image showing the distribution of the labeled molecules in space.Comment: 10 pages, 7 figure

Studies of discrete symmetries in decays of positronium atoms

A positronium - a bound state of electron and positron - is an eigenstate of parity and charge conjugation operators which decays into photons. It is a unique laboratory to study discrete symmetries whose precision is limited, in principle, by the effects due to the weak interactions expected at the level of 10−14 and photon-photon interactions expected at the level of 10−9. The Jagiellonian Positron Emission Tomograph (J-PET) is a detector for medical imaging as well as for physics studies involving detection of electronpositron annihilation into photons. The physics case covers the areas of discrete symmetries studies and genuine multipartite entanglement. The J-PET detector has high angular and time resolution and allows for determination of spin of the positronium and the momenta and polarization vectors of annihilation quanta. In this article, we present the potential of the J-PET system for studies of discrete symmetries in decays of positronium atoms