## Next Generation of Computational Sciences by HA-PACS

### 1. Particle Physics

#### Multi-scale physics

Investigate hierarchical properties via direct construction of nuclei in lattice QCD

GPU to solve large sparse linear systems of equations

#### Finite temperature and density

Phase analysis of QCD at finite temperature and density

GPU to perform matrix-matrix product of dense matrices

Expected QCD phase diagram

### 2. Astrophysics

#### (A) 6-Dimensional Computational Astrophysics

__Goals__

Elucidation of First Generation Objects

Elucidation of Cosmic Reionization

**6-Dimensional Radiation Hydrodynamics**:

3-Dimenasional Hydrodynamics + 6-Dimensional Radiation Transfer

– Acceleration by GPU of ray tracing and chemical reactions, which are of strong scaling

– Realization of radiation hydrodynamics

#### (B) Self-gravitational N-body CollisionlessSystems

__Goals__

Elucidation of Merger and Growth of Massive Black Holes

Elucidation of Globular Cluster Formation and Evolution

### 3. Nuclear Physics

#### Simulation with real-time and real-space method for many-fermion systems

**Nuclear response and reaction dynamics relevant to nucleosynthesis**

– Nuclear transfer reaction to producer-process nuclei experimentally

– Fusion reaction of light nuclei

– Systematic investigation of nuclear response function

**Application of nuclear methods to other fields**

– First-principles calculation for light-matter interaction

– Propagation of ultra-intense laser pulse

– Simulation for atto-second electron dynamics

**Methodology** : Time-dependent mean-field theory (TDDFT, TDHF, TDHFB) with real-time and 3D real-space method

**Merit of GPU calculation** : High performance calculation for the operation of Hamiltonian on orbital wave functions

TDHF simulation to produce neutron-rich nuclei by multi-nucleon transfer reaction

Atto-second electron dynamics in solid induced by ultrashort laser pulse

### 4. Materials Science

Develop a general numerical method to solve the time-dependent Schrodinger equation for many-electron quantum systems and use it to

– *understand* atomic, molecular and materials structures and their dynamics

– *search* a way to *control* the structures and dynamics in femtosecond (10^{-15} s) or even attosecond (10^{-18} s) time scales.

Holographic image of an electron wavepacket colliding with ionic core.

Controlling the XUV transparency by IR laser in attosecond time scale.

### 5. Bioscience

### 6. Geoenvironment

__Objectives__

– GPU application to the Next-Generation Atmospheric General Circulation Model **NICAM**

– GPU application to the Large Eddy Simulation **(LES)**

– GPU application to the 3D **Normal Mode Expansion** of the atmospheric state variables

__Expected Products __

**LES** model with 10 m spatial resolution may be developed by the **GPU** application

**NICAM** physical processes may be efficiently calculated by the **GPU** application

**Energetics analysis** of the high-resolution atmospheric GCM may be possible by the **GPU** application

__Merit of the GPU application__

**Weather forecasting model** by a grid discretization is a type of stencil computation. The memory access is therefore simple, and the computational acceleration up to 10 times speed may be possible by the **GPU** application.

Topography with a 50 m grids

### 7. Database

#### Data Mining of Big Data based on GPGPU

__Research objective and plan__

– **Accelerating data mining** from big data using **GPU**

– Target mining algorithms

– Document clustering

– PLSI (Probabilistic Latent Semantic Indexing)

– LDA (Latent Dirichret Allocation)

– Probabilistic association-rule mining

– Developed algorithms for single-GPU.

– Develop multi-GPU versions for GPU-cluster environment based on the current algorithms.

__Expected results and breakthrough__

– Application of GPU-cluster to problems other than numerical analysis or simulation.

– Few existing works have applied GPU-cluster to data mining problems so far.

– Promote the use of GPU-cluster as a platform for big data analysis.

__Applicability of GPU__

– Some data mining algorithms are suitable for GPU, but others may not.

– A technical challenge is to combine CPU- and GPU-based computation taking account of the algorithmic characteristics.

__Scale of computation__

– Under consideration

– Aiming at processing big datasets such that GPU-cluster is necessary.