SlideShare a Scribd company logo

QuantumChemistry500

•
•
Maho Nakata
Maho Nakata

QuantumChemsitry500は新しいスパコン用ベンチマークを提案します。ハイパフォーマンスコンピューティング、量子化学計算の両方に資することを目指しています。 * 現在のコンピューターでの最高の量子化学計算は何かをクリアにする。 * エクサスケールコンピューティングにおける、並列性を量子化学ではどう実現されているか、すべきかをクリアにに提示する。 * 真に有用なベンチマークを提供する。

Software
1 of 14
Download to read offline
QuantumChemistry500 NAKATA Maho (RIKEN) ISHIMURA Kazuya (IMS) HIRANO Toshiyuki (U of Tokyo) Jeff HAMMOND (Intel) 2014/11/18 Tue Nov 18 @ 12:15pm-1:15pm Room 295
Role of HPC Benchmarks • Represent important applications. • Based upon simple codes that non-experts (especially vendors) can optimize them. • Be somewhat orthogonal to each other. • Stress computer hardware in interesting ways. • Allow for objective comparison between different computing platforms.
Current HPC Benchmarks • Top500 = HPL: LU factorization (just DGEMM?). • Graph500: Non-numerical benchmark. • HPCG: Conjugate gradient PDE solver for simple stencil. • HPGMG: Geometric multigride PDE solver. • HPCChallenge: Collection of benchmarks. • STREAM • Scalable Synthetic Compact Applications (SSCA) • DOE Mini-apps • ...
Quantum Chemistry in HPC • QC/DFT major component of scientific workloads. Many QC apps are built by users and un-tracked. Figure courtesy of Richard Gerber (NERSC) VASP and NWChem build matrix differently. QC500 represents harder way.
What is QuantumChemistry 500? • Very different properties than existing benchmarks: – nontrivial load-balancing (irregular, dynamic tasks) – small- to mid-sized messages (between 8B and 100KB) – nontrivial to vectorize (short SIMD) – balance of memory- and compute- intensive – kernels contain branching – modest dense linear algebra (not HPL-sized) • Allows many implementations as long as same numerically. – Easier entry for novel hardware (VHDL impl???). • Optimized implementations already exist: NWChem, …, GTFOCK (OpenMP/SSE/AVX), TeraChem (GPU)
What is QuantumChemistry 500? • Chemistry-specific benchmark targeting most common method(s). Initial target is Hartree-Fock SCF (DFT-like). • Science-driven, scale-invariant focus: Performance per node/watt/etc… • Allows different algorithms and software as long as the answer is the same. • Building upon existing HPC codes for initial data; encourage new optimized code development. • Exercise hardware using challenging kernels not captured by any existing benchmark. • Avoid Goodhart’s Law (A machine built just for QC500 will be good at many things...)
Hartree-Fock/SCF/DFT Theory This is the classic algorithm; variations exist. ● Formation of matrix is irregular. ● Matrix elements highly non-trivial (3+ methods exist). ● Diagonalize via GEVP or DMM. Quantum chemists have implemented many algorithms in many software packages and yet it is possible to obtain numerical consistency between codes!
Reference inputs - Insulin - http://www.pdb.org/pdb/101/motm.do?momID=14 - PDBID: 2HIU - 51 AA - Antifreeze Proteins - http://www.rcsb.org/pdb/explore.do?structureId=2PNE - PDBID: 2PNE - 70 AA - Ubiquitin - http://www.pdb.org/pdb/101/motm.do?momID=60 - PDBID: 1UBQ - 76 AA - HIV-1 Protease - http://www.pdb.org/pdb/101/motm.do?momID=6 - PDBID: 7HVP - 99 AA 2HIU : Insulin 2PNE : antifreeze Protein
Input Specification • Given data as reference – Atomic coordinates, molecule charge and spin. – Basis set (cc-pVXZ – to allow for a range of problem sizes) – Sample input files for several quantum chemistry packages to enable data collection by operators. • Requirements for accuracy/precision of final results. • Hartree-Fock and DFT (B3LYP) • Other methods under investigation for the future.
Implementations • ACESIII • CFOUR • Dalton • FireFly • GAMESS • Gaussian • GTFock • Molpro • Molcas • NWChem • ORCA • ProteinDF • Psi4 • QChem • SMASH • TeraChem • TurboMole • etc. Submissions must include detailed algorithmic and implementation specification sufficient for reproduction in a different implementation. Numerical tolerances must be documented. The best specification includes complete source code.
Reference Results ● We will use GAMESS, NWChem, and ProteinDF to generate reference energy values. >>> They must agree to be a valid reference! ● These codes are free, parallel and widely supported by HPC folks. Involved in many procurements so vendors are familiar. ● Reference codes do not have a lot of approximations by default (no linear-scaling tricks).
Conditions for valid result • Converged total energy should match our reference value to six (?) decimal places in atomic unit. • Converged orbital energies should match our reference value to three (?) decimal places in atomic unit. • These criteria are open for debate. Some may argue for higher accuracy...
Results to be submitted • Elapsed time • Which program package is used & Input file – Changes from default (e.g., cutoff value) – Details of implementation and algorithms • Output file – Total energy and orbital energies • Machine configuration – CPU, memory, network, storage and their peak (theoretical) performance • All of above info will be open to the public
TODO until next year • Forming steering committee of scientific experts with deep HPC knowledge: • academia/national labs • industry (IBM, NVIDIA, …) • Digital presence • Reference data and validation infrastructure • The first benchmark results on several supercomputers.

Recommended

Some experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon PhiSome experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon Phi
Maho Nakata
 
Kobeworkshop pubchemqc project
Kobeworkshop pubchemqc projectKobeworkshop pubchemqc project
Kobeworkshop pubchemqc project
Maho Nakata
 
The PubChemQC Project
The PubChemQC ProjectThe PubChemQC Project
The PubChemQC Project
Maho Nakata
 
20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装
20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装
20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装
Maho Nakata
 
Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...
Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...
Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...
Intel® Software
 
Anaconda Python KNIME & Orange Installation
Anaconda Python KNIME & Orange InstallationAnaconda Python KNIME & Orange Installation
Anaconda Python KNIME & Orange Installation
Girinath Pillai
 
LEGaTO: Software Stack Runtimes
LEGaTO: Software Stack RuntimesLEGaTO: Software Stack Runtimes
LEGaTO: Software Stack Runtimes
LEGATO project
 
IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」
Preferred Networks
 

Recommended

Some experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon PhiSome experiences for porting application to Intel Xeon Phi
Some experiences for porting application to Intel Xeon Phi
Maho Nakata
 
Kobeworkshop pubchemqc project
Kobeworkshop pubchemqc projectKobeworkshop pubchemqc project
Kobeworkshop pubchemqc project
Maho Nakata
 
The PubChemQC Project
The PubChemQC ProjectThe PubChemQC Project
The PubChemQC Project
Maho Nakata
 
20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装
20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装
20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装
Maho Nakata
 
Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...
Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...
Use C++ and Intel® Threading Building Blocks (Intel® TBB) for Hardware Progra...
Intel® Software
 
Anaconda Python KNIME & Orange Installation
Anaconda Python KNIME & Orange InstallationAnaconda Python KNIME & Orange Installation
Anaconda Python KNIME & Orange Installation
Girinath Pillai
 
LEGaTO: Software Stack Runtimes
LEGaTO: Software Stack RuntimesLEGaTO: Software Stack Runtimes
LEGaTO: Software Stack Runtimes
LEGATO project
 
IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」IIBMP2019 講演資料「オープンソースで始める深層学習」
IIBMP2019 講演資料「オープンソースで始める深層学習」
Preferred Networks
 
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place SolutionKaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Preferred Networks
 
Profiling PyTorch for Efficiency & Sustainability
Profiling PyTorch for Efficiency & SustainabilityProfiling PyTorch for Efficiency & Sustainability
Profiling PyTorch for Efficiency & Sustainability
geetachauhan
 
強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷
Eiji Sekiya
 
On the Capability and Achievable Performance of FPGAs for HPC Applications
On the Capability and Achievable Performance of FPGAs for HPC ApplicationsOn the Capability and Achievable Performance of FPGAs for HPC Applications
On the Capability and Achievable Performance of FPGAs for HPC Applications
Wim Vanderbauwhede
 
Design and implementation of Parallel Prefix Adders using FPGAs
Design and implementation of Parallel Prefix Adders using FPGAsDesign and implementation of Parallel Prefix Adders using FPGAs
Design and implementation of Parallel Prefix Adders using FPGAs
IOSR Journals
 
An evaluation of LLVM compiler for SVE with fairly complicated loops
An evaluation of LLVM compiler for SVE with fairly complicated loopsAn evaluation of LLVM compiler for SVE with fairly complicated loops
An evaluation of LLVM compiler for SVE with fairly complicated loops
Linaro
 
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
Preferred Networks
 
Available HPC resources at CSUC
Available HPC resources at CSUCAvailable HPC resources at CSUC
Available HPC resources at CSUC
CSUC - Consorci de Serveis Universitaris de Catalunya
 
Real-time applications on IntelXeon/Phi
Real-time applications on IntelXeon/PhiReal-time applications on IntelXeon/Phi
Real-time applications on IntelXeon/Phi
Karel Ha
 
Cluster Schedulers
Cluster SchedulersCluster Schedulers
Cluster Schedulers
Pietro Michiardi
 
Improving Efficiency of Machine Learning Algorithms using HPCC Systems
Improving Efficiency of Machine Learning Algorithms using HPCC SystemsImproving Efficiency of Machine Learning Algorithms using HPCC Systems
Improving Efficiency of Machine Learning Algorithms using HPCC Systems
HPCC Systems
 
Parallel External Memory Algorithms Applied to Generalized Linear Models
Parallel External Memory Algorithms Applied to Generalized Linear ModelsParallel External Memory Algorithms Applied to Generalized Linear Models
Parallel External Memory Algorithms Applied to Generalized Linear Models
Revolution Analytics
 
Deep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistryDeep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistry
Kenta Oono
 
Making Hardware Accelerator Easier to Use
Making Hardware Accelerator Easier to UseMaking Hardware Accelerator Easier to Use
Making Hardware Accelerator Easier to Use
Kazuaki Ishizaki
 
Compilation of COSMO for GPU using LLVM
Compilation of COSMO for GPU using LLVMCompilation of COSMO for GPU using LLVM
Compilation of COSMO for GPU using LLVM
Linaro
 
GTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introductionGTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introduction
Kenta Oono
 
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
AMD Developer Central
 
AI is Impacting HPC Everywhere
AI is Impacting HPC EverywhereAI is Impacting HPC Everywhere
AI is Impacting HPC Everywhere
inside-BigData.com
 
Introduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKennaIntroduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKenna
openseesdays
 
A CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural NetworksA CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural Networks
Shinya Takamaeda-Y
 
Assisting User’s Transition to Titan’s Accelerated Architecture
Assisting User’s Transition to Titan’s Accelerated ArchitectureAssisting User’s Transition to Titan’s Accelerated Architecture
Assisting User’s Transition to Titan’s Accelerated Architecture
inside-BigData.com
 
참여기관_발표자료-국민대학교 201301 정기회의
참여기관_발표자료-국민대학교 201301 정기회의참여기관_발표자료-국민대학교 201301 정기회의
참여기관_발표자료-국민대학교 201301 정기회의
DzH QWuynh
 

More Related Content

What's hot

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place SolutionKaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Preferred Networks
 
Profiling PyTorch for Efficiency & Sustainability
Profiling PyTorch for Efficiency & SustainabilityProfiling PyTorch for Efficiency & Sustainability
Profiling PyTorch for Efficiency & Sustainability
geetachauhan
 
強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷
Eiji Sekiya
 
On the Capability and Achievable Performance of FPGAs for HPC Applications
On the Capability and Achievable Performance of FPGAs for HPC ApplicationsOn the Capability and Achievable Performance of FPGAs for HPC Applications
On the Capability and Achievable Performance of FPGAs for HPC Applications
Wim Vanderbauwhede
 
Design and implementation of Parallel Prefix Adders using FPGAs
Design and implementation of Parallel Prefix Adders using FPGAsDesign and implementation of Parallel Prefix Adders using FPGAs
Design and implementation of Parallel Prefix Adders using FPGAs
IOSR Journals
 
An evaluation of LLVM compiler for SVE with fairly complicated loops
An evaluation of LLVM compiler for SVE with fairly complicated loopsAn evaluation of LLVM compiler for SVE with fairly complicated loops
An evaluation of LLVM compiler for SVE with fairly complicated loops
Linaro
 
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
Preferred Networks
 
Available HPC resources at CSUC
Available HPC resources at CSUCAvailable HPC resources at CSUC
Available HPC resources at CSUC
CSUC - Consorci de Serveis Universitaris de Catalunya
 
Real-time applications on IntelXeon/Phi
Real-time applications on IntelXeon/PhiReal-time applications on IntelXeon/Phi
Real-time applications on IntelXeon/Phi
Karel Ha
 
Cluster Schedulers
Cluster SchedulersCluster Schedulers
Cluster Schedulers
Pietro Michiardi
 
Improving Efficiency of Machine Learning Algorithms using HPCC Systems
Improving Efficiency of Machine Learning Algorithms using HPCC SystemsImproving Efficiency of Machine Learning Algorithms using HPCC Systems
Improving Efficiency of Machine Learning Algorithms using HPCC Systems
HPCC Systems
 
Parallel External Memory Algorithms Applied to Generalized Linear Models
Parallel External Memory Algorithms Applied to Generalized Linear ModelsParallel External Memory Algorithms Applied to Generalized Linear Models
Parallel External Memory Algorithms Applied to Generalized Linear Models
Revolution Analytics
 
Deep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistryDeep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistry
Kenta Oono
 
Making Hardware Accelerator Easier to Use
Making Hardware Accelerator Easier to UseMaking Hardware Accelerator Easier to Use
Making Hardware Accelerator Easier to Use
Kazuaki Ishizaki
 
Compilation of COSMO for GPU using LLVM
Compilation of COSMO for GPU using LLVMCompilation of COSMO for GPU using LLVM
Compilation of COSMO for GPU using LLVM
Linaro
 
GTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introductionGTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introduction
Kenta Oono
 
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
AMD Developer Central
 
AI is Impacting HPC Everywhere
AI is Impacting HPC EverywhereAI is Impacting HPC Everywhere
AI is Impacting HPC Everywhere
inside-BigData.com
 
Introduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKennaIntroduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKenna
openseesdays
 
A CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural NetworksA CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural Networks
Shinya Takamaeda-Y
 

What's hot (20)

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place SolutionKaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution
 
Profiling PyTorch for Efficiency & Sustainability
Profiling PyTorch for Efficiency & SustainabilityProfiling PyTorch for Efficiency & Sustainability
Profiling PyTorch for Efficiency & Sustainability
 
強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷強化学習の分散アーキテクチャ変遷
強化学習の分散アーキテクチャ変遷
 
On the Capability and Achievable Performance of FPGAs for HPC Applications
On the Capability and Achievable Performance of FPGAs for HPC ApplicationsOn the Capability and Achievable Performance of FPGAs for HPC Applications
On the Capability and Achievable Performance of FPGAs for HPC Applications
 
Design and implementation of Parallel Prefix Adders using FPGAs
Design and implementation of Parallel Prefix Adders using FPGAsDesign and implementation of Parallel Prefix Adders using FPGAs
Design and implementation of Parallel Prefix Adders using FPGAs
 
An evaluation of LLVM compiler for SVE with fairly complicated loops
An evaluation of LLVM compiler for SVE with fairly complicated loopsAn evaluation of LLVM compiler for SVE with fairly complicated loops
An evaluation of LLVM compiler for SVE with fairly complicated loops
 
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
PFN Summer Internship 2019 / Kenshin Abe: Extension of Chainer-Chemistry for ...
 
Available HPC resources at CSUC
Available HPC resources at CSUCAvailable HPC resources at CSUC
Available HPC resources at CSUC
 
Real-time applications on IntelXeon/Phi
Real-time applications on IntelXeon/PhiReal-time applications on IntelXeon/Phi
Real-time applications on IntelXeon/Phi
 
Cluster Schedulers
Cluster SchedulersCluster Schedulers
Cluster Schedulers
 
Improving Efficiency of Machine Learning Algorithms using HPCC Systems
Improving Efficiency of Machine Learning Algorithms using HPCC SystemsImproving Efficiency of Machine Learning Algorithms using HPCC Systems
Improving Efficiency of Machine Learning Algorithms using HPCC Systems
 
Parallel External Memory Algorithms Applied to Generalized Linear Models
Parallel External Memory Algorithms Applied to Generalized Linear ModelsParallel External Memory Algorithms Applied to Generalized Linear Models
Parallel External Memory Algorithms Applied to Generalized Linear Models
 
Deep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistryDeep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistry
 
Making Hardware Accelerator Easier to Use
Making Hardware Accelerator Easier to UseMaking Hardware Accelerator Easier to Use
Making Hardware Accelerator Easier to Use
 
Compilation of COSMO for GPU using LLVM
Compilation of COSMO for GPU using LLVMCompilation of COSMO for GPU using LLVM
Compilation of COSMO for GPU using LLVM
 
GTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introductionGTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introduction
 
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...
 
AI is Impacting HPC Everywhere
AI is Impacting HPC EverywhereAI is Impacting HPC Everywhere
AI is Impacting HPC Everywhere
 
Introduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKennaIntroduction to OpenSees by Frank McKenna
Introduction to OpenSees by Frank McKenna
 
A CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural NetworksA CGRA-based Approach for Accelerating Convolutional Neural Networks
A CGRA-based Approach for Accelerating Convolutional Neural Networks
 

Similar to QuantumChemistry500

Assisting User’s Transition to Titan’s Accelerated Architecture
Assisting User’s Transition to Titan’s Accelerated ArchitectureAssisting User’s Transition to Titan’s Accelerated Architecture
Assisting User’s Transition to Titan’s Accelerated Architecture
inside-BigData.com
 
참여기관_발표자료-국민대학교 201301 정기회의
참여기관_발표자료-국민대학교 201301 정기회의참여기관_발표자료-국민대학교 201301 정기회의
참여기관_발표자료-국민대학교 201301 정기회의
DzH QWuynh
 
High performance computing for research
High performance computing for researchHigh performance computing for research
High performance computing for research
Esteban Hernandez
 
Realizing Robust and Scalable Evolutionary Algorithms toward Exascale Era
Realizing Robust and Scalable Evolutionary Algorithms toward Exascale EraRealizing Robust and Scalable Evolutionary Algorithms toward Exascale Era
Realizing Robust and Scalable Evolutionary Algorithms toward Exascale Era
Masaharu Munetomo
 
Early Application experiences on Summit
Early Application experiences on Summit Early Application experiences on Summit
Early Application experiences on Summit
Ganesan Narayanasamy
 
OpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC SystemsOpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC Systems
HPCC Systems
 
Parallelization of Coupled Cluster Code with OpenMP
Parallelization of Coupled Cluster Code with OpenMPParallelization of Coupled Cluster Code with OpenMP
Parallelization of Coupled Cluster Code with OpenMP
Anil Bohare
 
The state of SQL-on-Hadoop in the Cloud
The state of SQL-on-Hadoop in the CloudThe state of SQL-on-Hadoop in the Cloud
The state of SQL-on-Hadoop in the Cloud
DataWorks Summit/Hadoop Summit
 
Application Profiling at the HPCAC High Performance Center
Application Profiling at the HPCAC High Performance CenterApplication Profiling at the HPCAC High Performance Center
Application Profiling at the HPCAC High Performance Center
inside-BigData.com
 
05 Preparing for Extreme Geterogeneity in HPC
05 Preparing for Extreme Geterogeneity in HPC05 Preparing for Extreme Geterogeneity in HPC
05 Preparing for Extreme Geterogeneity in HPC
RCCSRENKEI
 
Current Trends in HPC
Current Trends in HPCCurrent Trends in HPC
Current Trends in HPC
Putchong Uthayopas
 
The CAOS framework: Democratize the acceleration of compute intensive applica...
The CAOS framework: Democratize the acceleration of compute intensive applica...The CAOS framework: Democratize the acceleration of compute intensive applica...
The CAOS framework: Democratize the acceleration of compute intensive applica...
NECST Lab @ Politecnico di Milano
 
In datacenter performance analysis of a tensor processing unit
In datacenter performance analysis of a tensor processing unitIn datacenter performance analysis of a tensor processing unit
In datacenter performance analysis of a tensor processing unit
Jinwon Lee
 
PEARC 17: Spark On the ARC
PEARC 17: Spark On the ARCPEARC 17: Spark On the ARC
PEARC 17: Spark On the ARC
Himanshu Bedi
 
Available HPC Resources at CSUC
Available HPC Resources at CSUCAvailable HPC Resources at CSUC
Available HPC Resources at CSUC
CSUC - Consorci de Serveis Universitaris de Catalunya
 
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
Choose Your Weapon: Comparing Spark on FPGAs vs GPUsChoose Your Weapon: Comparing Spark on FPGAs vs GPUs
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
Databricks
 
Introduction to multicore .ppt
Introduction to multicore .pptIntroduction to multicore .ppt
Introduction to multicore .ppt
Rajagopal Nagarajan
 
Post compiler software optimization for reducing energy
Post compiler software optimization for reducing energyPost compiler software optimization for reducing energy
Post compiler software optimization for reducing energy
Abhishek Abhyankar
 
RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...
RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...
RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...
OPAL-RT TECHNOLOGIES
 
Approximation techniques used for general purpose algorithms
Approximation techniques used for general purpose algorithmsApproximation techniques used for general purpose algorithms
Approximation techniques used for general purpose algorithms
Sabidur Rahman
 

Similar to QuantumChemistry500 (20)

Assisting User’s Transition to Titan’s Accelerated Architecture
Assisting User’s Transition to Titan’s Accelerated ArchitectureAssisting User’s Transition to Titan’s Accelerated Architecture
Assisting User’s Transition to Titan’s Accelerated Architecture
 
참여기관_발표자료-국민대학교 201301 정기회의
참여기관_발표자료-국민대학교 201301 정기회의참여기관_발표자료-국민대학교 201301 정기회의
참여기관_발표자료-국민대학교 201301 정기회의
 
High performance computing for research
High performance computing for researchHigh performance computing for research
High performance computing for research
 
Realizing Robust and Scalable Evolutionary Algorithms toward Exascale Era
Realizing Robust and Scalable Evolutionary Algorithms toward Exascale EraRealizing Robust and Scalable Evolutionary Algorithms toward Exascale Era
Realizing Robust and Scalable Evolutionary Algorithms toward Exascale Era
 
Early Application experiences on Summit
Early Application experiences on Summit Early Application experiences on Summit
Early Application experiences on Summit
 
OpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC SystemsOpenPOWER Acceleration of HPCC Systems
OpenPOWER Acceleration of HPCC Systems
 
Parallelization of Coupled Cluster Code with OpenMP
Parallelization of Coupled Cluster Code with OpenMPParallelization of Coupled Cluster Code with OpenMP
Parallelization of Coupled Cluster Code with OpenMP
 
The state of SQL-on-Hadoop in the Cloud
The state of SQL-on-Hadoop in the CloudThe state of SQL-on-Hadoop in the Cloud
The state of SQL-on-Hadoop in the Cloud
 
Application Profiling at the HPCAC High Performance Center
Application Profiling at the HPCAC High Performance CenterApplication Profiling at the HPCAC High Performance Center
Application Profiling at the HPCAC High Performance Center
 
05 Preparing for Extreme Geterogeneity in HPC
05 Preparing for Extreme Geterogeneity in HPC05 Preparing for Extreme Geterogeneity in HPC
05 Preparing for Extreme Geterogeneity in HPC
 
Current Trends in HPC
Current Trends in HPCCurrent Trends in HPC
Current Trends in HPC
 
The CAOS framework: Democratize the acceleration of compute intensive applica...
The CAOS framework: Democratize the acceleration of compute intensive applica...The CAOS framework: Democratize the acceleration of compute intensive applica...
The CAOS framework: Democratize the acceleration of compute intensive applica...
 
In datacenter performance analysis of a tensor processing unit
In datacenter performance analysis of a tensor processing unitIn datacenter performance analysis of a tensor processing unit
In datacenter performance analysis of a tensor processing unit
 
PEARC 17: Spark On the ARC
PEARC 17: Spark On the ARCPEARC 17: Spark On the ARC
PEARC 17: Spark On the ARC
 
Available HPC Resources at CSUC
Available HPC Resources at CSUCAvailable HPC Resources at CSUC
Available HPC Resources at CSUC
 
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
Choose Your Weapon: Comparing Spark on FPGAs vs GPUsChoose Your Weapon: Comparing Spark on FPGAs vs GPUs
Choose Your Weapon: Comparing Spark on FPGAs vs GPUs
 
Introduction to multicore .ppt
Introduction to multicore .pptIntroduction to multicore .ppt
Introduction to multicore .ppt
 
Post compiler software optimization for reducing energy
Post compiler software optimization for reducing energyPost compiler software optimization for reducing energy
Post compiler software optimization for reducing energy
 
RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...
RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...
RT15 Berkeley | Introduction to FPGA Power Electronic & Electric Machine real...
 
Approximation techniques used for general purpose algorithms
Approximation techniques used for general purpose algorithmsApproximation techniques used for general purpose algorithms
Approximation techniques used for general purpose algorithms
 

More from Maho Nakata

quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)
quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)
quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)
Maho Nakata
 
Lie-Trotter-Suzuki分解、特にフラクタル分解について
Lie-Trotter-Suzuki分解、特にフラクタル分解についてLie-Trotter-Suzuki分解、特にフラクタル分解について
Lie-Trotter-Suzuki分解、特にフラクタル分解について
Maho Nakata
 
LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編
LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編
LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編
Maho Nakata
 
Q#による量子化学計算 : 水素分子の位相推定について
Q#による量子化学計算 : 水素分子の位相推定についてQ#による量子化学計算 : 水素分子の位相推定について
Q#による量子化学計算 : 水素分子の位相推定について
Maho Nakata
 
量子コンピュータの量子化学計算への応用の現状と展望
量子コンピュータの量子化学計算への応用の現状と展望量子コンピュータの量子化学計算への応用の現状と展望
量子コンピュータの量子化学計算への応用の現状と展望
Maho Nakata
 
qubitによる波動関数の虚時間発展のシミュレーション: a review
qubitによる波動関数の虚時間発展のシミュレーション: a reviewqubitによる波動関数の虚時間発展のシミュレーション: a review
qubitによる波動関数の虚時間発展のシミュレーション: a review
Maho Nakata
 
Openfermionを使った分子の計算 part I
Openfermionを使った分子の計算 part IOpenfermionを使った分子の計算 part I
Openfermionを使った分子の計算 part I
Maho Nakata
 
量子コンピュータで量子化学のfullCIが超高速になる(かも
量子コンピュータで量子化学のfullCIが超高速になる(かも量子コンピュータで量子化学のfullCIが超高速になる(かも
量子コンピュータで量子化学のfullCIが超高速になる(かも
Maho Nakata
 
第11回分子科学 2017/9/17 Pubchemqcプロジェクト
第11回分子科学 2017/9/17 Pubchemqcプロジェクト第11回分子科学 2017/9/17 Pubchemqcプロジェクト
第11回分子科学 2017/9/17 Pubchemqcプロジェクト
Maho Nakata
 
計算化学実習講座:第二回
計算化学実習講座:第二回 計算化学実習講座:第二回
計算化学実習講座:第二回
Maho Nakata
 
計算化学実習講座:第一回
計算化学実習講座:第一回計算化学実習講座:第一回
計算化学実習講座:第一回
Maho Nakata
 
HOKUSAIのベンチマーク 理研シンポジウム 中田分
HOKUSAIのベンチマーク 理研シンポジウム 中田分HOKUSAIのベンチマーク 理研シンポジウム 中田分
HOKUSAIのベンチマーク 理研シンポジウム 中田分
Maho Nakata
 
為替取引(FX)でのtickdataの加工とMySQLで管理
為替取引(FX)でのtickdataの加工とMySQLで管理為替取引(FX)でのtickdataの加工とMySQLで管理
為替取引(FX)でのtickdataの加工とMySQLで管理
Maho Nakata
 
為替のTickdataをDukascopyからダウンロードする
為替のTickdataをDukascopyからダウンロードする為替のTickdataをDukascopyからダウンロードする
為替のTickdataをDukascopyからダウンロードする
Maho Nakata
 
HPCS2015 pythonを用いた量子化学プログラムの開発と応用
HPCS2015 pythonを用いた量子化学プログラムの開発と応用HPCS2015 pythonを用いた量子化学プログラムの開発と応用
HPCS2015 pythonを用いた量子化学プログラムの開発と応用
Maho Nakata
 
HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)
HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)
HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)
Maho Nakata
 
3Dプリンタ導入記　タンパク質の模型をプリントする
3Dプリンタ導入記　タンパク質の模型をプリントする3Dプリンタ導入記　タンパク質の模型をプリントする
3Dプリンタ導入記　タンパク質の模型をプリントする
Maho Nakata
 
立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1
立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1 立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1
立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1
Maho Nakata
 
The PubchemQC project
The PubchemQC projectThe PubchemQC project
The PubchemQC project
Maho Nakata
 
Direct variational calculation of second-order reduced density matrix : appli...
Direct variational calculation of second-order reduced density matrix : appli...Direct variational calculation of second-order reduced density matrix : appli...
Direct variational calculation of second-order reduced density matrix : appli...
Maho Nakata
 

More from Maho Nakata (20)

quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)
quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)
quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)
 
Lie-Trotter-Suzuki分解、特にフラクタル分解について
Lie-Trotter-Suzuki分解、特にフラクタル分解についてLie-Trotter-Suzuki分解、特にフラクタル分解について
Lie-Trotter-Suzuki分解、特にフラクタル分解について
 
LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編
LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編
LiHのポテンシャルエネルギー曲面 を量子コンピュータで行う Q#+位相推定編
 
Q#による量子化学計算 : 水素分子の位相推定について
Q#による量子化学計算 : 水素分子の位相推定についてQ#による量子化学計算 : 水素分子の位相推定について
Q#による量子化学計算 : 水素分子の位相推定について
 
量子コンピュータの量子化学計算への応用の現状と展望
量子コンピュータの量子化学計算への応用の現状と展望量子コンピュータの量子化学計算への応用の現状と展望
量子コンピュータの量子化学計算への応用の現状と展望
 
qubitによる波動関数の虚時間発展のシミュレーション: a review
qubitによる波動関数の虚時間発展のシミュレーション: a reviewqubitによる波動関数の虚時間発展のシミュレーション: a review
qubitによる波動関数の虚時間発展のシミュレーション: a review
 
Openfermionを使った分子の計算 part I
Openfermionを使った分子の計算 part IOpenfermionを使った分子の計算 part I
Openfermionを使った分子の計算 part I
 
量子コンピュータで量子化学のfullCIが超高速になる(かも
量子コンピュータで量子化学のfullCIが超高速になる(かも量子コンピュータで量子化学のfullCIが超高速になる(かも
量子コンピュータで量子化学のfullCIが超高速になる(かも
 
第11回分子科学 2017/9/17 Pubchemqcプロジェクト
第11回分子科学 2017/9/17 Pubchemqcプロジェクト第11回分子科学 2017/9/17 Pubchemqcプロジェクト
第11回分子科学 2017/9/17 Pubchemqcプロジェクト
 
計算化学実習講座:第二回
計算化学実習講座:第二回 計算化学実習講座:第二回
計算化学実習講座:第二回
 
計算化学実習講座:第一回
計算化学実習講座:第一回計算化学実習講座:第一回
計算化学実習講座:第一回
 
HOKUSAIのベンチマーク 理研シンポジウム 中田分
HOKUSAIのベンチマーク 理研シンポジウム 中田分HOKUSAIのベンチマーク 理研シンポジウム 中田分
HOKUSAIのベンチマーク 理研シンポジウム 中田分
 
為替取引(FX)でのtickdataの加工とMySQLで管理
為替取引(FX)でのtickdataの加工とMySQLで管理為替取引(FX)でのtickdataの加工とMySQLで管理
為替取引(FX)でのtickdataの加工とMySQLで管理
 
為替のTickdataをDukascopyからダウンロードする
為替のTickdataをDukascopyからダウンロードする為替のTickdataをDukascopyからダウンロードする
為替のTickdataをDukascopyからダウンロードする
 
HPCS2015 pythonを用いた量子化学プログラムの開発と応用
HPCS2015 pythonを用いた量子化学プログラムの開発と応用HPCS2015 pythonを用いた量子化学プログラムの開発と応用
HPCS2015 pythonを用いた量子化学プログラムの開発と応用
 
HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)
HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)
HPCS2015 大規模量子化学計算プログラムSMASHの開発と公開(石村)
 
3Dプリンタ導入記　タンパク質の模型をプリントする
3Dプリンタ導入記　タンパク質の模型をプリントする3Dプリンタ導入記　タンパク質の模型をプリントする
3Dプリンタ導入記　タンパク質の模型をプリントする
 
立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1
立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1 立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1
立教大学化学実験3 SMILESを中心とした高度な分子モデリング 2014/7/1
 
The PubchemQC project
The PubchemQC projectThe PubchemQC project
The PubchemQC project
 
Direct variational calculation of second-order reduced density matrix : appli...
Direct variational calculation of second-order reduced density matrix : appli...Direct variational calculation of second-order reduced density matrix : appli...
Direct variational calculation of second-order reduced density matrix : appli...
 

Recently uploaded

YAML crash COURSE how to write yaml file for adding configuring details
YAML crash COURSE how to write yaml file for adding configuring detailsYAML crash COURSE how to write yaml file for adding configuring details
YAML crash COURSE how to write yaml file for adding configuring details
NishanthaBulumulla1
 
E-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet DynamicsE-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet Dynamics
Hornet Dynamics
 
All you need to know about Spring Boot and GraalVM
All you need to know about Spring Boot and GraalVMAll you need to know about Spring Boot and GraalVM
All you need to know about Spring Boot and GraalVM
Alina Yurenko
 
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
mz5nrf0n
 
14 th Edition of International conference on computer vision
14 th Edition of International conference on computer vision14 th Edition of International conference on computer vision
14 th Edition of International conference on computer vision
ShulagnaSarkar2
 
Using Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query PerformanceUsing Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query Performance
Grant Fritchey
 
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
mz5nrf0n
 
J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...
J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...
J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...
Bert Jan Schrijver
 
一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理
dakas1
 
Energy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina JonuziEnergy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina Jonuzi
Green Software Development
 
Malibou Pitch Deck For Its €3M Seed Round
Malibou Pitch Deck For Its €3M Seed RoundMalibou Pitch Deck For Its €3M Seed Round
Malibou Pitch Deck For Its €3M Seed Round
sjcobrien
 
Preparing Non - Technical Founders for Engaging a Tech Agency
Preparing Non - Technical Founders for Engaging a Tech AgencyPreparing Non - Technical Founders for Engaging a Tech Agency
Preparing Non - Technical Founders for Engaging a Tech Agency
ISH Technologies
 
WWDC 2024 Keynote Review: For CocoaCoders Austin
WWDC 2024 Keynote Review: For CocoaCoders AustinWWDC 2024 Keynote Review: For CocoaCoders Austin
WWDC 2024 Keynote Review: For CocoaCoders Austin
Patrick Weigel
 
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdfTop Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
VALiNTRY360
 
Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language ProcessorsFundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
Rakesh Kumar R
 
How Can Hiring A Mobile App Development Company Help Your Business Grow?
How Can Hiring A Mobile App Development Company Help Your Business Grow?How Can Hiring A Mobile App Development Company Help Your Business Grow?
How Can Hiring A Mobile App Development Company Help Your Business Grow?
ToXSL Technologies
 
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdfUnveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
brainerhub1
 
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, FactsALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Green Software Development
 
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemUI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
Peter Muessig
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
Octavian Nadolu
 

Recently uploaded (20)

YAML crash COURSE how to write yaml file for adding configuring details
YAML crash COURSE how to write yaml file for adding configuring detailsYAML crash COURSE how to write yaml file for adding configuring details
YAML crash COURSE how to write yaml file for adding configuring details
 
E-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet DynamicsE-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet Dynamics
 
All you need to know about Spring Boot and GraalVM
All you need to know about Spring Boot and GraalVMAll you need to know about Spring Boot and GraalVM
All you need to know about Spring Boot and GraalVM
 
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
 
14 th Edition of International conference on computer vision
14 th Edition of International conference on computer vision14 th Edition of International conference on computer vision
14 th Edition of International conference on computer vision
 
Using Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query PerformanceUsing Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query Performance
 
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
 
J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...
J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...
J-Spring 2024 - Going serverless with Quarkus, GraalVM native images and AWS ...
 
一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理一比一原版(USF毕业证)旧金山大学毕业证如何办理
一比一原版(USF毕业证)旧金山大学毕业证如何办理
 
Energy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina JonuziEnergy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina Jonuzi
 
Malibou Pitch Deck For Its €3M Seed Round
Malibou Pitch Deck For Its €3M Seed RoundMalibou Pitch Deck For Its €3M Seed Round
Malibou Pitch Deck For Its €3M Seed Round
 
Preparing Non - Technical Founders for Engaging a Tech Agency
Preparing Non - Technical Founders for Engaging a Tech AgencyPreparing Non - Technical Founders for Engaging a Tech Agency
Preparing Non - Technical Founders for Engaging a Tech Agency
 
WWDC 2024 Keynote Review: For CocoaCoders Austin
WWDC 2024 Keynote Review: For CocoaCoders AustinWWDC 2024 Keynote Review: For CocoaCoders Austin
WWDC 2024 Keynote Review: For CocoaCoders Austin
 
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdfTop Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
 
Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language ProcessorsFundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
 
How Can Hiring A Mobile App Development Company Help Your Business Grow?
How Can Hiring A Mobile App Development Company Help Your Business Grow?How Can Hiring A Mobile App Development Company Help Your Business Grow?
How Can Hiring A Mobile App Development Company Help Your Business Grow?
 
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdfUnveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
 
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, FactsALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
 
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemUI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
 

QuantumChemistry500

  • 1. QuantumChemistry500 NAKATA Maho (RIKEN) ISHIMURA Kazuya (IMS) HIRANO Toshiyuki (U of Tokyo) Jeff HAMMOND (Intel) 2014/11/18 Tue Nov 18 @ 12:15pm-1:15pm Room 295
  • 2. Role of HPC Benchmarks • Represent important applications. • Based upon simple codes that non-experts (especially vendors) can optimize them. • Be somewhat orthogonal to each other. • Stress computer hardware in interesting ways. • Allow for objective comparison between different computing platforms.
  • 3. Current HPC Benchmarks • Top500 = HPL: LU factorization (just DGEMM?). • Graph500: Non-numerical benchmark. • HPCG: Conjugate gradient PDE solver for simple stencil. • HPGMG: Geometric multigride PDE solver. • HPCChallenge: Collection of benchmarks. • STREAM • Scalable Synthetic Compact Applications (SSCA) • DOE Mini-apps • ...
  • 4. Quantum Chemistry in HPC • QC/DFT major component of scientific workloads. Many QC apps are built by users and un-tracked. Figure courtesy of Richard Gerber (NERSC) VASP and NWChem build matrix differently. QC500 represents harder way.
  • 5. What is QuantumChemistry 500? • Very different properties than existing benchmarks: – nontrivial load-balancing (irregular, dynamic tasks) – small- to mid-sized messages (between 8B and 100KB) – nontrivial to vectorize (short SIMD) – balance of memory- and compute- intensive – kernels contain branching – modest dense linear algebra (not HPL-sized) • Allows many implementations as long as same numerically. – Easier entry for novel hardware (VHDL impl???). • Optimized implementations already exist: NWChem, …, GTFOCK (OpenMP/SSE/AVX), TeraChem (GPU)
  • 6. What is QuantumChemistry 500? • Chemistry-specific benchmark targeting most common method(s). Initial target is Hartree-Fock SCF (DFT-like). • Science-driven, scale-invariant focus: Performance per node/watt/etc… • Allows different algorithms and software as long as the answer is the same. • Building upon existing HPC codes for initial data; encourage new optimized code development. • Exercise hardware using challenging kernels not captured by any existing benchmark. • Avoid Goodhart’s Law (A machine built just for QC500 will be good at many things...)
  • 7. Hartree-Fock/SCF/DFT Theory This is the classic algorithm; variations exist. ● Formation of matrix is irregular. ● Matrix elements highly non-trivial (3+ methods exist). ● Diagonalize via GEVP or DMM. Quantum chemists have implemented many algorithms in many software packages and yet it is possible to obtain numerical consistency between codes!
  • 8. Reference inputs - Insulin - http://www.pdb.org/pdb/101/motm.do?momID=14 - PDBID: 2HIU - 51 AA - Antifreeze Proteins - http://www.rcsb.org/pdb/explore.do?structureId=2PNE - PDBID: 2PNE - 70 AA - Ubiquitin - http://www.pdb.org/pdb/101/motm.do?momID=60 - PDBID: 1UBQ - 76 AA - HIV-1 Protease - http://www.pdb.org/pdb/101/motm.do?momID=6 - PDBID: 7HVP - 99 AA 2HIU : Insulin 2PNE : antifreeze Protein
  • 9. Input Specification • Given data as reference – Atomic coordinates, molecule charge and spin. – Basis set (cc-pVXZ – to allow for a range of problem sizes) – Sample input files for several quantum chemistry packages to enable data collection by operators. • Requirements for accuracy/precision of final results. • Hartree-Fock and DFT (B3LYP) • Other methods under investigation for the future.
  • 10. Implementations • ACESIII • CFOUR • Dalton • FireFly • GAMESS • Gaussian • GTFock • Molpro • Molcas • NWChem • ORCA • ProteinDF • Psi4 • QChem • SMASH • TeraChem • TurboMole • etc. Submissions must include detailed algorithmic and implementation specification sufficient for reproduction in a different implementation. Numerical tolerances must be documented. The best specification includes complete source code.
  • 11. Reference Results ● We will use GAMESS, NWChem, and ProteinDF to generate reference energy values. >>> They must agree to be a valid reference! ● These codes are free, parallel and widely supported by HPC folks. Involved in many procurements so vendors are familiar. ● Reference codes do not have a lot of approximations by default (no linear-scaling tricks).
  • 12. Conditions for valid result • Converged total energy should match our reference value to six (?) decimal places in atomic unit. • Converged orbital energies should match our reference value to three (?) decimal places in atomic unit. • These criteria are open for debate. Some may argue for higher accuracy...
  • 13. Results to be submitted • Elapsed time • Which program package is used & Input file – Changes from default (e.g., cutoff value) – Details of implementation and algorithms • Output file – Total energy and orbital energies • Machine configuration – CPU, memory, network, storage and their peak (theoretical) performance • All of above info will be open to the public
  • 14. TODO until next year • Forming steering committee of scientific experts with deep HPC knowledge: • academia/national labs • industry (IBM, NVIDIA, …) • Digital presence • Reference data and validation infrastructure • The first benchmark results on several supercomputers.