Workshop: (Recent Progresses on Quantum Information Sciencs and Technology)

Introduction to Q-Center in South Korea

The Q-center has been founded in mid-2020 under the support of National Research Foundation of Korea. This talk not only presents a brief introduction of main functionalities & activities of the center including the latest ongoing effort made to provide quantum cloud services to (domestic) researchers, but discusses diverse upcoming action plans for supporting R&D and educational activities in the field of quantum information science & engineering.

Workshop: (Recent Progresses on Quantum Information Sciencs and Technology)

Tutorial: Quantum Computing with IBM QISKIT

QISKIT is an open-source development framework for quantum computing. It provides tools for creating and manipulating programs running on IBM-powered gate-based quantum computer (Q Experience) or on simulators in a local computer. Presenting a great chance for researchers in the field of computer science to learn the basic concept of quantum computing and how they can be realized for a python-based programming, this 2-hour tutorial will be conducted in two sessions: (1) Fundamentals and (2) Introduction to Quantum Algorithms.

Workshop: (Recent Progresses on Quantum Information Sciencs and Technology)

Tutorial: Quantum Computing with IBM QISKIT

QISKIT is an open-source development framework for quantum computing. It provides tools for creating and manipulating programs running on IBM-powered gate-based quantum computer (Q Experience) or on simulators in a local computer. Presenting a great chance for researchers in the field of computer science to learn the basic concept of quantum computing and how they can be realized for a python-based programming, this 2-hour tutorial will be conducted in two sessions: (1) Fundamentals and (2) Introduction to Quantum Algorithms.

Workshop: (Recent Progresses on Quantum Information Sciencs and Technology)

Learning Simulator for Quantum Algorithm Designs

In this talk, we introduce a machine-learning-based method to ‘learn’ a (structure of) quantum algorithm. The essence of the method is to use a classical-quantum hybrid learning simulator, where a quantum system is being taught by a classical learning algorithm. Our learning simulator is applied to learn a quantum algorithm for solving a function decision problem, called as a Deutsch problem. It is demonstrated by the numerical simulations that our simulator can always learn a quantum algorithm which is corresponding to, however not exactly equal to, the original Deutsch’s quantum algorithm. The most remarkable result is that the learning time is proportional to square-root of the search-space dimension, in contrast to an exponential (or polynomial at best) tendency found in classical learning. This is because the learning simulator reflects the (quantum) speedup of the algorithm identified in its (classical) learning.

Workshop: (Recent Progresses on Quantum Information Sciencs and Technology)

Quantum Biology: Numerically Exact Simulation of Open Quantum System Dynamics

In experiments, quantum mechanical systems are not completely isolated from their environments, and it is important to develop a theoretical framework for treating system-environment couplings in a reliable and efficient way. In this talk, I will discuss the theory of open quantum systems and its application to spontaneous emission from a two-level atom where radiative environments are well approximated by a Markovian bath. In addition, I will report recent theoretical efforts towards numerically exact simulations of photosynthetic pigment-protein complexes, where electronic excitations are coupled to highly structured vibrational environments at finite temperatures, leading to non-Markovian effects, and perturbation theory cannot be used.

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Opening Remarks

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Keynote Address: Advancing HPC Together

(TBD)

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

High Performance Simulations of Quantum Transport using Manycore Computing

The Non-Equilibrium Green’s Function (NEGF) has been widely utilized in the field of nanoscience and nanotechnology to predict carrier transport behaviors in electronic device channels of sizes in a quantum regime. This work explores how much performance improvement can be driven for NEGF computations with unique features of manycore computing, where the core numerical step of NEGF computations involves a recursive process of matrix-matrix multiplication. The major techniques adopted for the performance enhancement are data-restructuring, matrix-tiling, thread-scheduling, and offload computing and we present in-depth discussion on why they are critical to fully exploit the power of manycore computing hardware including Intel Xeon Phi Knights Landing systems and NVIDIA general-purpose graphic processing unit (GPU) devices. Performance of the optimized algorithm has been tested in a single computing node, where the host is Xeon Phi 7210 that is equipped with two NVIDIA Quadro GV100 GPU devices. The target structure of NEGF simulations is a [100] silicon nanowire that consists of 100K atoms involving a 1000K×1000K complex Hamiltonian matrix. Through rigorous benchmark tests, we show, with optimization techniques whose details are elaborately explained, the workload can be accelerated almost by a factor of up to ∼20 compared to the unoptimized case.

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Distributed MLPerf ResNet50 Training on Intel Xeon Architectures with TensorFlow

MLPerf benchmarks, which measure training and inference performance of ML hardware and software, have published three sets of ML training results so far. In all sets of results, ResNet50v1.5 was used as a standard benchmark to showcase the latest developments on image recognition tasks. The latest MLPerf training round (v0.7) featured Intel’s submission with TensorFlow. In this paper, we describe the recent optimization work that enabled this submission. In particular, we enabled BFloat16 data type in ResNet50v1.5 model as well as in Intel-optimized TensorFlow to exploit full potential of 3rd generation Intel Xeon scalable processors that have built-in BFloat16 support. We also describe the performance optimizations as well as the state-of-the-art accuracy/convergence results of ResNet50v1.5 model, achieved with large-scale distributed training (with up to 256 MPI workers) with Horovod. These results lay great foundation to support future MLPerf training submissions with large scale Intel Xeon clusters.

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Invited Talk: oneAPI Industry Initiative for Accelerated Computing

(TBD)

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Single-Precision Calculation of Iterative Refinement of Eigenpairs of a Real Symmetric-Definite Generalized Eigenproblem by Using a Filter Composed of a Single Resolvent

By using a filter, we calculate approximate eigenpairs of a real symmetric-definite generalized eigenproblem 𝐴v = 𝜆𝐵v whose eigenvalues are in a specified interval. In our experiments in this paper, the IEEE-754 single-precision floating-point (binary 32bit) number system is used for calculations. In general, a filter is constructed by using some resolvents R(𝜌) with different shifts 𝜌. For a given vector x, an action of a resolvent y := R(𝜌)x is given by solving a system of linear equations 𝐶(𝜌)y = 𝐵x for y, here the coefficient 𝐶(𝜌) =𝐴−𝜌𝐵 is symmetric. We assume to solve this system of linear equations by matrix factorization of 𝐶(𝜌), for example by the modified Cholesky method (𝐿𝐷𝐿^𝑇 decomposition method). When both matrices 𝐴 and 𝐵 are banded, 𝐶(𝜌) is also banded and the modified Cholesky method for banded system can be used to solve the system of linear equations. The filter we used is either a polynomial of a resolvent with a real shift, or a polynomial of an imaginary part of a resolvent with an imaginary shift. We use only a single resolvent to construct the filter in order to reduce both amounts of calculation to factor matrices and especially storage to hold factors of matrices. The most disadvantage when we use only a single resolvent rather than many is, such a filter have poor properties especially when compuation is made in single-precision. Therefore, approximate eigenpairs required are not obtained in good accuracy if they are extracted from the set of vectors made by an application of a combination of 𝐵-orthonormalization and filtering to a set of initial random vectors. However, experiments show approximate eigenpairs required are refined well if they are extracted from the set of vectors obtained by a few applications of a combination of 𝐵-orthonormalization and filtering to a set of initial random vectors.

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

A Comparison of Parallel Profiling Tools for Programs Utilizing the FFT

Performance monitoring is an important component of code optimization. Performance monitoring is also important for the beginning user, but can be difficult to configure appropriately. The overhead of the performance monitoring tools Craypat, FPMP, mpiP, Scalasca and TAU, are measured using default configurations likely to be chosen by a novice user and shown to be small when profiling Fast Fourier Transform based solvers for the Klein Gordon equation based on 2decomp&FFT and on FFTE. Performance measurements help explain that despite FFTE having a more efficient parallel algorithm, it is not always faster than 2decom&FFT because the complied single core FFT is not as fast as that in FFTW which is used in 2decomp&FFT.

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Efficient Parallel Multigrid Method on Intel Xeon Phi Clusters

The parallel multigrid method is expected to play an important role in scientific computing on exa-scale supercomputer systems for solving large-scale linear equations with sparse matrices. Because solving sparse linear systems is a very memory-bound process, efficient method for storage of coefficient matrices is a crucial issue. In the previous works, authors implemented sliced ELL method to parallel conjugate gradient solvers with multigrid preconditioning (MGCG) for the application on 3D groundwater flow through heterogeneous porous media (pGW3D-FVM), and excellent performance has been obtained on large-scale multicore/manycore clusters. In the present work, authors introduced SELL-C-s to the MGCG solver, and evaluated the performance of the solver with various types of OpenMP/MPI hybrid parallel programing models on the Oakforest-PACS (OFP) system at JCAHPC using up to 1,024 nodes of Intel Xeon Phi. Because SELL-C-s is suitable for wide-SIMD architecture, such as Xeon Phi, improvement of the performance over the sliced ELL was more than 20%. This is one of the first examples of SELL-C-s applied to forward/backward substitutions in ILU-type smoother of multigrid solver. Furthermore, effects of IHK/McKernel has been investigated, and it achieved 11% improvement on 1,024 nodes.

Workshop: (Intel eXtreme Performance Users Group, IXPUG)

Closing Remarks