73 research outputs found
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 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
Investigating the Dirac operator evaluation with FPGAs
In recent years the computational capacity of single Field Programmable Gate
Arrays (FPGA) devices as well as their versatility has increased significantly.
Adding to that the High Level Synthesis frameworks allowing to program such
processors in a high level language like C++, makes modern FPGA devices a
serious candidate as building blocks of a general purpose High Performance
Computing solution. In this contribution we describe benchmarks which we
performed using a Lattice QCD code, a highly compute-demanding HPC academic
code for elementary particle simulations. We benchmark the performance of a
single FPGA device running in two modes: using the external or embedded memory.
We discuss both approaches in detail using the Xilinx U250 device and provide
estimates for the necessary memory throughput and the minimal amount of
resources needed to deliver optimal performance depending on the available
hardware platform.Comment: 8 pages, 5 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
Solving Lattice QCD systems of equations using mixed precision solvers on GPUs
Modern graphics hardware is designed for highly parallel numerical tasks and
promises significant cost and performance benefits for many scientific
applications. One such application is lattice quantum chromodyamics (lattice
QCD), where the main computational challenge is to efficiently solve the
discretized Dirac equation in the presence of an SU(3) gauge field. Using
NVIDIA's CUDA platform we have implemented a Wilson-Dirac sparse matrix-vector
product that performs at up to 40 Gflops, 135 Gflops and 212 Gflops for double,
single and half precision respectively on NVIDIA's GeForce GTX 280 GPU. We have
developed a new mixed precision approach for Krylov solvers using reliable
updates which allows for full double precision accuracy while using only single
or half precision arithmetic for the bulk of the computation. The resulting
BiCGstab and CG solvers run in excess of 100 Gflops and, in terms of iterations
until convergence, perform better than the usual defect-correction approach for
mixed precision.Comment: 30 pages, 7 figure
Recent development and perspectives of machines for lattice QCD
I highlight recent progress in cluster computer technology and assess status
and prospects of cluster computers for lattice QCD with respect to the
development of QCDOC and apeNEXT. Taking the LatFor test case, I specify a
512-processor QCD-cluster better than 1$/Mflops.Comment: 14 pages, 17 figures, Lattice2003(plenary
The QPACE Supercomputer : Applications of Random Matrix Theory in Two-Colour Quantum Chromodynamics
QPACE is a massively parallel and scalable supercomputer designed to meet the requirements of applications in Lattice Quantum Chromodynamics. The project was carried out by several academic institutions in collaboration with IBM Germany and other industrial partners. In November 2009 and June 2010
QPACE was the leading architecture on the Green 500 list of the most energy efficient supercomputers in the world
CompF2: Theoretical Calculations and Simulation Topical Group Report
This report summarizes the work of the Computational Frontier topical group
on theoretical calculations and simulation for Snowmass 2021. We discuss the
challenges, potential solutions, and needs facing six diverse but related
topical areas that span the subject of theoretical calculations and simulation
in high energy physics (HEP): cosmic calculations, particle accelerator
modeling, detector simulation, event generators, perturbative calculations, and
lattice QCD (quantum chromodynamics). The challenges arise from the next
generations of HEP experiments, which will include more complex instruments,
provide larger data volumes, and perform more precise measurements.
Calculations and simulations will need to keep up with these increased
requirements. The other aspect of the challenge is the evolution of computing
landscape away from general-purpose computing on CPUs and toward
special-purpose accelerators and coprocessors such as GPUs and FPGAs. These
newer devices can provide substantial improvements for certain categories of
algorithms, at the expense of more specialized programming and memory and data
access patterns.Comment: Report of the Computational Frontier Topical Group on Theoretical
Calculations and Simulation for Snowmass 202
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