Changes

=== Assignment 1 - Select and Assess ===

Blaise Barney introduced Parallel Computing https://computing.llnl.gov/tutorials/parallel_comp/#ExamplesArrayArray 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.

==== Zhijian ====