9c28faa149b75de91f60a2f91d40b9ce49bd1b8c
1 //**************************************************************************
2 // Multi-threaded Matrix Multiply benchmark
3 //--------------------------------------------------------------------------
4 // TA : Christopher Celio
8 // This benchmark multiplies two 2-D arrays together and writes the results to
9 // a third vector. The input data (and reference data) should be generated
10 // using the matmul_gendata.pl perl script and dumped to a file named
14 // print out arrays, etc.
17 //--------------------------------------------------------------------------
25 //--------------------------------------------------------------------------
26 // Input/Reference Data
32 //--------------------------------------------------------------------------
33 // Basic Utilities and Multi-thread Support
35 __thread
unsigned long coreid
;
40 #define stringify_1(s) #s
41 #define stringify(s) stringify_1(s)
42 #define stats(code) do { \
43 unsigned long _c = -rdcycle(), _i = -rdinstret(); \
45 _c += rdcycle(), _i += rdinstret(); \
47 printf("%s: %ld cycles, %ld.%ld cycles/iter, %ld.%ld CPI\n", \
48 stringify(code), _c, _c/DIM_SIZE/DIM_SIZE/DIM_SIZE, 10*_c/DIM_SIZE/DIM_SIZE/DIM_SIZE%10, _c/_i, 10*_c/_i%10); \
52 //--------------------------------------------------------------------------
55 void printArray( char name
[], int n
, data_t arr
[] )
61 printf( " %10s :", name
);
62 for ( i
= 0; i
< n
; i
++ )
63 printf( " %3ld ", (long) arr
[i
] );
67 void __attribute__((noinline
)) verify(size_t n
, const data_t
* test
, const data_t
* correct
)
73 for (i
= 0; i
< n
; i
++)
75 if (test
[i
] != correct
[i
])
77 printf("FAILED test[%d]= %3ld, correct[%d]= %3ld\n",
78 i
, (long)test
[i
], i
, (long)correct
[i
]);
86 data_t
mult(data_t x
, data_t y
)
89 for (i
=0; i
< x
; i
++) {
94 //--------------------------------------------------------------------------
97 // single-thread, naive version
98 void __attribute__((noinline
)) matmul_naive(const int lda
, const data_t A
[], const data_t B
[], data_t C
[] )
105 for ( i
= 0; i
< lda
; i
++ )
106 for ( j
= 0; j
< lda
; j
++ )
108 for ( k
= 0; k
< lda
; k
++ )
110 C
[i
+ j
*lda
] += A
[j
*lda
+ k
] * B
[k
*lda
+ i
];
117 void __attribute__((noinline
)) matmul(const int lda
, const data_t A
[], const data_t B
[], data_t C
[] )
120 int row
,row2
, row3
, row4
, column
, column2
, column3
, column4
, column5
, column6
, column7
, column8
;
121 data_t element
, element2
, element3
, element4
, element5
, element6
, element7
, element8
;
122 data_t element9
, element10
, element11
, element12
, element13
, element14
, element15
, element16
;
123 data_t elementB1
,elementB2
,elementB3
,elementB4
;
124 data_t temp_mat
[128]={0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
125 //data_t temp_mat2[32]={0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
126 //for (i=coreid*max_dim/ncores; i<(max_dim/ncores+coreid*max_dim/ncores); i+=8){
127 for (l
=coreid
*32/ncores
; l
<32*(1+coreid
)/ncores
; l
+=4){
132 for (i
=0; i
<lda
; i
+=4){
134 element2
= A
[row
+i
+1];
135 element3
= A
[row
+i
+2];
136 element4
= A
[row
+i
+3];
138 element5
= A
[row2
+i
];
139 element6
= A
[row2
+i
+1];
140 element7
= A
[row2
+i
+2];
141 element8
= A
[row2
+i
+3];
143 element9
= A
[row3
+i
];
144 element10
= A
[row3
+i
+1];
145 element11
= A
[row3
+i
+2];
146 element12
= A
[row3
+i
+3];
148 element13
= A
[row4
+i
];
149 element14
= A
[row4
+i
+1];
150 element15
= A
[row4
+i
+2];
151 element16
= A
[row4
+i
+3];
159 for (j
=0; j
<lda
; j
+=4){
161 temp_mat
[j
]+=element
*B
[column
+j
]+element2
*B
[column2
+j
]+element3
*B
[column3
+j
]+element4
*B
[column4
+j
];
162 temp_mat
[j
+1]+=element
*B
[column
+j
+1]+element2
*B
[column2
+j
+1]+element3
*B
[column3
+j
+1]+element4
*B
[column4
+j
+1];
163 temp_mat
[j
+2]+=element
*B
[column
+j
+2]+element2
*B
[column2
+j
+2]+element3
*B
[column3
+j
+2]+element4
*B
[column4
+j
+2];
164 temp_mat
[j
+3]+=element
*B
[column
+j
+3]+element2
*B
[column2
+j
+3]+element3
*B
[column3
+j
+3]+element4
*B
[column4
+j
+3];
166 temp_mat
[j
+lda
]+=element5
*B
[column
+j
]+element6
*B
[column2
+j
]+element7
*B
[column3
+j
]+element8
*B
[column4
+j
];
167 temp_mat
[j
+1+lda
]+=element5
*B
[column
+j
+1]+element6
*B
[column2
+j
+1]+element7
*B
[column3
+j
+1]+element8
*B
[column4
+j
+1];
168 temp_mat
[j
+2+lda
]+=element5
*B
[column
+j
+2]+element6
*B
[column2
+j
+2]+element7
*B
[column3
+j
+2]+element8
*B
[column4
+j
+2];
169 temp_mat
[j
+3+lda
]+=element5
*B
[column
+j
+3]+element6
*B
[column2
+j
+3]+element7
*B
[column3
+j
+3]+element8
*B
[column4
+j
+3];
171 temp_mat
[j
+2*lda
]+=element9
*B
[column
+j
]+element10
*B
[column2
+j
]+element11
*B
[column3
+j
]+element12
*B
[column4
+j
];
172 temp_mat
[j
+1+2*lda
]+=element9
*B
[column
+j
+1]+element10
*B
[column2
+j
+1]+element11
*B
[column3
+j
+1]+element12
*B
[column4
+j
+1];
173 temp_mat
[j
+2+2*lda
]+=element9
*B
[column
+j
+2]+element10
*B
[column2
+j
+2]+element11
*B
[column3
+j
+2]+element12
*B
[column4
+j
+2];
174 temp_mat
[j
+3+2*lda
]+=element9
*B
[column
+j
+3]+element10
*B
[column2
+j
+3]+element11
*B
[column3
+j
+3]+element12
*B
[column4
+j
+3];
176 temp_mat
[j
+3*lda
]+=element13
*B
[column
+j
]+element14
*B
[column2
+j
]+element15
*B
[column3
+j
]+element16
*B
[column4
+j
];
177 temp_mat
[j
+1+3*lda
]+=element13
*B
[column
+j
+1]+element14
*B
[column2
+j
+1]+element15
*B
[column3
+j
+1]+element16
*B
[column4
+j
+1];
178 temp_mat
[j
+2+3*lda
]+=element13
*B
[column
+j
+2]+element14
*B
[column2
+j
+2]+element15
*B
[column3
+j
+2]+element16
*B
[column4
+j
+2];
179 temp_mat
[j
+3+3*lda
]+=element13
*B
[column
+j
+3]+element14
*B
[column2
+j
+3]+element15
*B
[column3
+j
+3]+element16
*B
[column4
+j
+3];
187 C
[row
+k
]=temp_mat
[k
];
189 C
[row2
+k
]=temp_mat
[k
+lda
];
191 C
[row3
+k
]=temp_mat
[k
+2*lda
];
193 C
[row4
+k
]=temp_mat
[k
+3*lda
];
201 // ***************************** //
202 // **** ADD YOUR CODE HERE ***** //
203 // ***************************** //
205 // feel free to make a separate function for MI and MSI versions.
208 //--------------------------------------------------------------------------
211 // all threads start executing thread_entry(). Use their "coreid" to
212 // differentiate between threads (each thread is running on a separate core).
214 void thread_entry(int cid
, int nc
)
219 // static allocates data in the binary, which is visible to both threads
220 static data_t results_data
[ARRAY_SIZE
];
223 // Execute the provided, naive matmul
225 stats(matmul_naive(DIM_SIZE
, input1_data
, input2_data
, results_data
); barrier());
229 verify(ARRAY_SIZE
, results_data
, verify_data
);
231 // clear results from the first trial
234 for (i
=0; i
< ARRAY_SIZE
; i
++)
239 // Execute your faster matmul
241 stats(matmul(DIM_SIZE
, input1_data
, input2_data
, results_data
); barrier());
244 printArray("results:", ARRAY_SIZE
, results_data
);
245 printArray("verify :", ARRAY_SIZE
, verify_data
);
249 verify(ARRAY_SIZE
, results_data
, verify_data
);