• Home
  • History
  • Annotate
  • only in /external/eigen/bench/btl/
NameDateSize

..11-Jun-20184 KiB

actions/11-Jun-20184 KiB

cmake/11-Jun-20184 KiB

CMakeLists.txt11-Jun-20182.8 KiB

COPYING11-Jun-201817.7 KiB

data/11-Jun-20184 KiB

generic_bench/11-Jun-20184 KiB

libs/11-Jun-20184 KiB

README11-Jun-20186.3 KiB

README

1Bench Template Library
2
3****************************************
4Introduction :
5
6The aim of this project is to compare the performance
7of available numerical libraries. The code is designed
8as generic and modular as possible. Thus, adding new
9numerical libraries or new numerical tests should
10require minimal effort.
11
12
13*****************************************
14
15Installation :
16
17BTL uses cmake / ctest:
18
191 - create a build directory:
20
21  $ mkdir build
22  $ cd build
23
242 - configure:
25
26  $ ccmake ..
27
283 - run the bench using ctest:
29
30  $ ctest -V
31
32You can run the benchmarks only on libraries matching a given regular expression:
33  ctest -V -R <regexp>
34For instance:
35  ctest -V -R eigen2
36
37You can also select a given set of actions defining the environment variable BTL_CONFIG this way:
38  BTL_CONFIG="-a action1{:action2}*" ctest -V
39An exemple:
40  BTL_CONFIG="-a axpy:vector_matrix:trisolve:ata" ctest -V -R eigen2
41
42Finally, if bench results already exist (the bench*.dat files) then they merges by keeping the best for each matrix size. If you want to overwrite the previous ones you can simply add the "--overwrite" option:
43  BTL_CONFIG="-a axpy:vector_matrix:trisolve:ata --overwrite" ctest -V -R eigen2
44
454 : Analyze the result. different data files (.dat) are produced in each libs directories.
46 If gnuplot is available, choose a directory name in the data directory to store the results and type:
47        $ cd data
48        $ mkdir my_directory
49        $ cp ../libs/*/*.dat my_directory
50 Build the data utilities in this (data) directory
51        make
52 Then you can look the raw data,
53        go_mean my_directory
54 or smooth the data first :
55	smooth_all.sh my_directory
56	go_mean my_directory_smooth
57
58
59*************************************************
60
61Files and directories :
62
63 generic_bench : all the bench sources common to all libraries
64
65 actions : sources for different action wrappers (axpy, matrix-matrix product) to be tested.
66
67 libs/* : bench sources specific to each tested libraries.
68
69 machine_dep : directory used to store machine specific Makefile.in
70
71 data : directory used to store gnuplot scripts and data analysis utilities
72
73**************************************************
74
75Principles : the code modularity is achieved by defining two concepts :
76
77 ****** Action concept : This is a class defining which kind
78  of test must be performed (e.g. a matrix_vector_product).
79	An Action should define the following methods :
80
81        *** Ctor using the size of the problem (matrix or vector size) as an argument
82	    Action action(size);
83        *** initialize : this method initialize the calculation (e.g. initialize the matrices and vectors arguments)
84	    action.initialize();
85	*** calculate : this method actually launch the calculation to be benchmarked
86	    action.calculate;
87	*** nb_op_base() : this method returns the complexity of the calculate method (allowing the mflops evaluation)
88        *** name() : this method returns the name of the action (std::string)
89
90 ****** Interface concept : This is a class or namespace defining how to use a given library and
91  its specific containers (matrix and vector). Up to now an interface should following types
92
93	*** real_type : kind of float to be used (float or double)
94	*** stl_vector : must correspond to std::vector<real_type>
95	*** stl_matrix : must correspond to std::vector<stl_vector>
96	*** gene_vector : the vector type for this interface        --> e.g. (real_type *) for the C_interface
97	*** gene_matrix : the matrix type for this interface        --> e.g. (gene_vector *) for the C_interface
98
99	+ the following common methods
100
101        *** free_matrix(gene_matrix & A, int N)  dealocation of a N sized gene_matrix A
102        *** free_vector(gene_vector & B)  dealocation of a N sized gene_vector B
103        *** matrix_from_stl(gene_matrix & A, stl_matrix & A_stl) copy the content of an stl_matrix A_stl into a gene_matrix A.
104	     The allocation of A is done in this function.
105	*** vector_to_stl(gene_vector & B, stl_vector & B_stl)  copy the content of an stl_vector B_stl into a gene_vector B.
106	     The allocation of B is done in this function.
107        *** matrix_to_stl(gene_matrix & A, stl_matrix & A_stl) copy the content of an gene_matrix A into an stl_matrix A_stl.
108             The size of A_STL must corresponds to the size of A.
109        *** vector_to_stl(gene_vector & A, stl_vector & A_stl) copy the content of an gene_vector A into an stl_vector A_stl.
110             The size of B_STL must corresponds to the size of B.
111	*** copy_matrix(gene_matrix & source, gene_matrix & cible, int N) : copy the content of source in cible. Both source
112		and cible must be sized NxN.
113	*** copy_vector(gene_vector & source, gene_vector & cible, int N) : copy the content of source in cible. Both source
114 		and cible must be sized N.
115
116	and the following method corresponding to the action one wants to be benchmarked :
117
118	***  matrix_vector_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
119	***  matrix_matrix_product(const gene_matrix & A, const gene_matrix & B, gene_matrix & X, int N)
120        ***  ata_product(const gene_matrix & A, gene_matrix & X, int N)
121	***  aat_product(const gene_matrix & A, gene_matrix & X, int N)
122        ***  axpy(real coef, const gene_vector & X, gene_vector & Y, int N)
123
124 The bench algorithm (generic_bench/bench.hh) is templated with an action itself templated with
125 an interface. A typical main.cpp source stored in a given library directory libs/A_LIB
126 looks like :
127
128 bench< AN_ACTION < AN_INTERFACE > >( 10 , 1000 , 50 ) ;
129
130 this function will produce XY data file containing measured  mflops as a function of the size for 50
131 sizes between 10 and 10000.
132
133 This algorithm can be adapted by providing a given Perf_Analyzer object which determines how the time
134 measurements must be done. For example, the X86_Perf_Analyzer use the asm rdtsc function and provides
135 a very fast and accurate (but less portable) timing method. The default is the Portable_Perf_Analyzer
136 so
137
138 bench< AN_ACTION < AN_INTERFACE > >( 10 , 1000 , 50 ) ;
139
140 is equivalent to
141
142 bench< Portable_Perf_Analyzer,AN_ACTION < AN_INTERFACE > >( 10 , 1000 , 50 ) ;
143
144 If your system supports it we suggest to use a mixed implementation (X86_perf_Analyzer+Portable_Perf_Analyzer).
145 replace
146     bench<Portable_Perf_Analyzer,Action>(size_min,size_max,nb_point);
147 with
148     bench<Mixed_Perf_Analyzer,Action>(size_min,size_max,nb_point);
149 in generic/bench.hh
150
151.
152
153
154
155