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GTC ON-DEMAND

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Abstract:
Learn how combining GPUs with advanced multi-grid solvers are revolutionizing the study of lattice quantum chromodynamics (LQCD). LQCD is a computational tool for probing nuclear and particle physics, however, it can require thousands of GPUs working in tandem for months due to the computationally prohibitive linear solver. Using the QUDA framework, we describe how the solver can be accelerated using an adaptive multi-grid method. The optimization techniques employed are: fine-grained parallelization, mixed precision, communication reducing solvers, and reformulation of the algorithm to allow the CPU and GPU to work in parallel. Using this multitude of algorithmic innovations, we demonstrate that a 5X speedup can be realized over present state-of-the-art methods using GPUs.
Learn how combining GPUs with advanced multi-grid solvers are revolutionizing the study of lattice quantum chromodynamics (LQCD). LQCD is a computational tool for probing nuclear and particle physics, however, it can require thousands of GPUs working in tandem for months due to the computationally prohibitive linear solver. Using the QUDA framework, we describe how the solver can be accelerated using an adaptive multi-grid method. The optimization techniques employed are: fine-grained parallelization, mixed precision, communication reducing solvers, and reformulation of the algorithm to allow the CPU and GPU to work in parallel. Using this multitude of algorithmic innovations, we demonstrate that a 5X speedup can be realized over present state-of-the-art methods using GPUs.  Back
 
Topics:
Computational Physics, Algorithms & Numerical Techniques, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6667
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Abstract:
Learn how to leverage the power of GPUs to accelerate solution of large sparse linear systems with multiple right hand sides by means of the incremental eigCG algorithm. For a given hermitian system with multiple right hand sides this algorithm allows (1) to compute incrementally a number of small magnitude eigenvalues and corresponding eigenvectors while solving the first few systems with standard Conjugate Gradient (CG), and then (2) to reuse the computed eigenvectors to deflate the CG solver for the remaining systems. In this session we will discuss implementation aspects of the technique and analyse its efficiency on the example of lattice QCD fermion matrix inversions.
Learn how to leverage the power of GPUs to accelerate solution of large sparse linear systems with multiple right hand sides by means of the incremental eigCG algorithm. For a given hermitian system with multiple right hand sides this algorithm allows (1) to compute incrementally a number of small magnitude eigenvalues and corresponding eigenvectors while solving the first few systems with standard Conjugate Gradient (CG), and then (2) to reuse the computed eigenvectors to deflate the CG solver for the remaining systems. In this session we will discuss implementation aspects of the technique and analyse its efficiency on the example of lattice QCD fermion matrix inversions.   Back
 
Topics:
Computational Physics, Numerical Algorithms & Libraries, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4693
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Abstract:
Lattice QCD (LQCD) simulations are critical for understanding the validity of the Standard Model and the results of the High-Energy and Nuclear Physics experiments. Major improvements in the calculation and prediction of physical observables, such as nucleon form factors or flavor singlet meson mass, require large amounts of computer resources, of the order of hundreds of Tflop/s of sustained performance. We extend the QUDA library, an open source library for performing calculations in LQCD on NVIDIA GPUs, to include kernels for the non-degenerate twisted mass fermion operator which allows to utilize GPUs for a wider set of problems in LQCD.
Lattice QCD (LQCD) simulations are critical for understanding the validity of the Standard Model and the results of the High-Energy and Nuclear Physics experiments. Major improvements in the calculation and prediction of physical observables, such as nucleon form factors or flavor singlet meson mass, require large amounts of computer resources, of the order of hundreds of Tflop/s of sustained performance. We extend the QUDA library, an open source library for performing calculations in LQCD on NVIDIA GPUs, to include kernels for the non-degenerate twisted mass fermion operator which allows to utilize GPUs for a wider set of problems in LQCD.   Back
 
Topics:
Computational Physics
Type:
Poster
Event:
GTC Silicon Valley
Year:
2013
Session ID:
P3265
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