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A Cholesky Out-of-Core factorizationPowerPoint Presentation

A Cholesky Out-of-Core factorization

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A Cholesky Out-of-Core factorization. Jorge Castellanos Germán Larrazábal. Centro Multidisciplinario de Visualización y Cómputo Científico (CEMVICC) Facultad Experimental de Ciencias y Tecnología (FACYT) Universidad de Carabobo Valencia - Venezuela. Out-of-core - motivation.

A Cholesky Out-of-Core factorization

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A Cholesky Out-of-Core factorization

Jorge Castellanos Germán Larrazábal

Centro Multidisciplinario de Visualización y Cómputo Científico (CEMVICC)

Facultad Experimental de Ciencias y Tecnología (FACYT)

Universidad de Carabobo

Valencia - Venezuela

Out-of-core - motivation

The computational kernel that consumes the more of CPU time in a engineer numerical simulation package is the linear solver

The linear solver is mainly based on a classic solving method, i.e., direct or iterative method

The problem matrix associated to the equations system is sparse and have a big size

Iterative methods are highly parallel (based on matrix-vector product) but they are not general and they need preconditioning

Direct methods (Cholesky, LDLT or LU) are more general (inputs: matrix A and rhs vector b), but they main advantage is the amount of memory needed when the problem size A increases

Out-of-core - motivation

- Efficient numerical solutions of linear system of equations can show limitations when the associated set of matrices does not fit into memory
- Data can not be stored in memory, therefore it must be stored in hard disk
- Disk access is very slow (latency+bandwidth) compared to memory access
- In order to have good performance, the algorithm should (Toledo, 1999):
- Carry out the storage in terms of consecutive large blocks
- Use the data stored in memory as many times as possible

Out-of-core - definition

- Algorithms which are designed to have efficient performance when their data structure is stored in disk are called “out-of-core algorithms” (Toledo, 1999).
- Out-of core applications handle very large data sets to store in conventional internal memory
- There are a group of applications (parallel out-of-core) with data sets whose address space exceeds the capacity of the virtual memory
- Examples:
- Scientific computing (modeling, simulation, etc.)
- Scientific visualization
- Database

The out-of-core concept permits users to solve efficiently large problems using inexpensive computers

Storage in disk is cheaper than storage in main memory (DRAM) and its actual cost rate is 1 to 100 (dec, 2007)

An out-of-core algorithm running on a machine with a limited memory can give a better cost/performance ratio than in-core algorithms running on a machine with enough memory

In 1984, J. Reid creates the TREESOLV, written in fortran to solve big linear equation sytems based on the multifrontal algorithm

In 1999, E. Rothberg and R. Shreiber, review the implementation of 3 out-of-core methods for the Cholesky factorization. Each is based on a partitioning of the matrix into panels

En 2009, J. Reid and J. Scott present a Cholesky Out-of-core solver written in fortran 95, whose operation is based on a virtual memory package that provides the facilities to read/write hard disk files

Out-of-core support - proposal

To incorporate into UCSparseLib library a low level software layer that frees the user from worrying about memory constraints

The low level software layer will handle the I/O operations automatically

The support includes: caches, prefetching, multithreading, among other features to obtain good computational performance

The out-of-core support manages the memory

The implementation makes use of “in-core” coding included in the UCSparseLib library

- UCSparseLib (Larrazabal, 2004) has a set of functionalities for solving sparse linear systems
- The library handles and stores sparse matrices using a compact format similar to:
- CRS (Compressed Row Storage)
- CCS (Compressed Column Storage)

- It was designed with an out-of-core perspective

- PDVSA - INTEVEP S. A.
- Oil reservoir simulator (SEMIYA)

- CSRC (Computational Sciences Research Center), SDSU, USA
- Ocean simulator
- Computational fluid dynamic

- ULA
- Portal of damage project

- CEMVICC
- CFD projects

I/O operations: read and write matrices in a single format

Matrix-Vector operations: basic operations, vector-vector, matrix-vector, reordering, etc.

Direct and Iterative methods: Cholesky, LDLT, LU, Jacobi, Gauss-Seidel, Conjugate Gradient, GMRES, etc.

Preconditioners: Incomplete factorizations

Algebraic multigrid: AMG with different setup phases: aggregation, red-black colouring, strong connection

Eigenvalues: eigenvalues and eigenvectors for sparse symmetric matrices

Another routines: Timers, memory management and debugger

In order to store matrices temporally in memory (cache), the matrix is divided in 2n blocks

Each block can have 2m rows (CRS)

Example: the matrix is divided into 22 blocks; each block has 21 rows, except the last one that has 1 row

Each matrix is stored in a temporary binary file

Each row (col) is stored in a modified CRS (CCS) format, i.e., indices first and values next

Rows (cols) are transferred from (to) disk (memory) grouped in blocks

pos

temporary file

for (ii= 0; ii< nn; ii++)

{

For_OOCMatrix_Row( M, ii, rowI, READ_WRITE )

{

for(kk= 0; kk< rowI.diag; kk++) {jj = rowI.id[kk]

For_OOCMatrix_Row( M, jj, rowJ, READ ) {

Code to evaluate rowI.val[kk]

}

}

}

}

READ_WRITE

READ

temporary file

temporary file

To support the nesting of ForOOCMatrix_Row macros, the cache used in earlier versions was modified to have multiple ways

To mitigate the effect of misses increasing caused by the nested macro, a Reference Prediction Table (RPT) based on the proposal of Chen & Baer (1994) was incorporated

To ensure that the input/output cache data worked in parallel with computation to hide the latency caused by misses and pre-fetches an Outstanding Request List based on the scheme proposed by D. Kroft (1987) was implemented

Intel Core 2 Duo P8600™ 2.4 Ghz, 4GB DRAM, GNU/Linuxkernel 2.6.28-11-SMP, gcc 4.3 x86_64, -O2 -funroll-loops -fprefetch-loop-arrays

Matrices characteristics

Use of memory and execution time

Notes: Required Memory in bytes, time in seconds

Intel Core 2 Duo P8600™ 2.4 Ghz, 4GB DRAM, GNU/Linuxkernel 2.6.28-11-SMP, gcc 4.3 x86_64, -O2 -funroll-loops -fprefetch-loop-arrays

Prefetch performance

Input matrix

Output matrix

Notes: Required Memory in bytes, time in seconds

M: Generated by discretization of 3D scalar elliptic operator

The out-of-core kernel supports efficiently the Cholesky sparse matrix factorization because it shows big saves in memory use with overheads less than the 148% in CPU time

For a better performance of the out-of-core support, we believe it is important to have a high efficiency prefetch algorithm, as in this work, which reduces the adverse effect of cache misses

The prefetch algorithm must operate in parallel with the computation to take advantage of multicore technology present in most of the modern personal computers

To incorporate improvements in the parallelization of the prefetch algorithm to reduce the penalty in execution time

To study the effect of the block size of the cache and the total size of the cache (number of blocks) in order to define heuristics for automatic selection of these parameters

To extend the use of the out-of-core layer to other UCSparseLib library functions, including direct methods: LU and LDLT

To implement the out-of-core support for other methods of solving sparse linear systems such as iterative methods and algebraic multigrid

J. Castellanos and G. Larrazábal, Implementación out-of-core para producto matriz-vector y transpuesta de matrices dispersas, Conferencia Latinoamericana de Computación de Alto Rendimiento, Santa Marta, Colombia. Pags. 250--256. ISBN: 978--958--708--299--9, 2007.

J. Castellanos and G. Larrazábal, Soporte out-of-core para operaciones básicas con matrices dispersas, En Desarrollo y avances en métodos numéricos para ingeniería y ciencias aplicadas, Sociedad Venezolana de Métodos Numéricos en Ingeniería, Caracas, Venezuela. ISBN: 978--980--7161--00--8, 2008.

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G. Karypis and V. Kumar, A Fast and Highly Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM Journal on Scientific Computing, Vol. 20, No. 1, pp. 359—392, 1999.

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G. Larrazábal, Técnicas algebráicas de precondicionamiento para la resolución de sistemas lineales, Departamento de Arquitectura de Computadores (DAC), Universidad Politécnica de Cataluña, Barcelona, Spain. Tesis Doctoral ISBN: 84--688--1572--1, 2002.

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