# Difference between revisions of "Group 6"

(→Array Processing) |
(→Zhijian) |
||

Line 54: | Line 54: | ||

As the calculation of elements is independent of one another - leads to an embarrassingly parallel solution. Arrays elements are evenly distributed so that each process owns a portion of the array (subarray). It can be solved in less time with multiple compute resources than with a single compute resource. | As the calculation of elements is independent of one another - leads to an embarrassingly parallel solution. Arrays elements are evenly distributed so that each process owns a portion of the array (subarray). It can be solved in less time with multiple compute resources than with a single compute resource. | ||

− | === The Monte Carlo Simulation (PI Calculation) === | + | ==== The Monte Carlo Simulation (PI Calculation) ==== |

Subject: The Monte Carlo Simulation (PI Calculation) | Subject: The Monte Carlo Simulation (PI Calculation) | ||

Got the code from here: | Got the code from here: | ||

Line 66: | Line 66: | ||

[[File:Yihang.JPG]] | [[File:Yihang.JPG]] | ||

− | === Zhijian === | + | ==== Zhijian ==== |

Subject: | Subject: | ||

## Revision as of 23:58, 16 March 2019

GPU610/DPS915 | Student List | Group and Project Index | Student Resources | Glossary

## Contents

# Group 6

## Team Members

## Progress

### Assignment 1 - Select and Assess

#### Array Processing

Subject: Array Processing

Blaise Barney introduced Parallel Computing https://computing.llnl.gov/tutorials/parallel_comp/ Array processing could become one of the parallel example, which "demonstrates calculations on 2-dimensional array elements; a function is evaluated on each array element."

Standard random method is used to initialize a 2-dimentional array. The purpose of this program is to perform a 2-dimension array calculation, which is a matrix-matrix multiplication in this example.

In this following profile example, n = 1000

Flat profile:

Each sample counts as 0.01 seconds.

% cumulative self self total time seconds seconds calls Ts/call Ts/call name

100.11 1.48 1.48 multiply(float**, float**, float**, int)

0.68 1.49 0.01 init(float**, int) 0.00 1.49 0.00 1 0.00 0.00 _GLOBAL__sub_I__Z4initPPfi

Call graph

granularity: each sample hit covers 2 byte(s) for 0.67% of 1.49 seconds

index % time self children called name

<spontaneous>

[1] 99.3 1.48 0.00 multiply(float**, float**, float**, int) [1]

<spontaneous>

[2] 0.7 0.01 0.00 init(float**, int) [2]

0.00 0.00 1/1 __libc_csu_init [16]

[10] 0.0 0.00 0.00 1 _GLOBAL__sub_I__Z4initPPfi [10]

� Index by function name

[10] _GLOBAL__sub_I__Z4initPPfi (arrayProcessing.cpp) [2] init(float**, int) [1] multiply(float**, float**, float**, int)

From the call graph, multiply() took major runtime more than 99%, as it contains 3 for-loop, which is O(n^3). Besides, init() also became the second busy one, which has a O(n^2).

As the calculation of elements is independent of one another - leads to an embarrassingly parallel solution. Arrays elements are evenly distributed so that each process owns a portion of the array (subarray). It can be solved in less time with multiple compute resources than with a single compute resource.

#### The Monte Carlo Simulation (PI Calculation)

Subject: The Monte Carlo Simulation (PI Calculation) Got the code from here: https://rosettacode.org/wiki/Monte_Carlo_methods#C.2B.2B A Monte Carlo Simulation is a way of approximating the value of a function where calculating the actual value is difficult or impossible.

It uses random sampling to define constraints on the value and then makes a sort of "best guess."

#### Zhijian

Subject: