Group 6

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Group 6

Team Members

  1. Xiaowei Huang
  2. Yihang Yuan
  3. Zhijian Zhou

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Assignment 1 - Select and Assess

Array Processing

Subject: Array Processing

Blaise Barney introduced Parallel Computing 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


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


[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: 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."




Assignment 2

Assignment 3