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 printArrayMT( 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
)) verifyMT(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 //--------------------------------------------------------------------------
89 // single-thread, naive version
90 void __attribute__((noinline
)) matmul_naive(const int lda
, const data_t A
[], const data_t B
[], data_t C
[] )
97 for ( i
= 0; i
< lda
; i
++ )
98 for ( j
= 0; j
< lda
; j
++ )
100 for ( k
= 0; k
< lda
; k
++ )
102 C
[i
+ j
*lda
] += A
[j
*lda
+ k
] * B
[k
*lda
+ i
];
110 void __attribute__((noinline
)) matmul(const int lda
, const data_t A
[], const data_t B
[], data_t C
[] )
115 size_t max_dim = 32*32;
116 data_t temp_mat[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};
117 for (i=coreid*max_dim/ncores; i<(max_dim/ncores+coreid*max_dim/ncores); i+=8){
119 data_t element2 = A[i+1];
120 data_t element3 = A[i+2];
121 data_t element4 = A[i+3];
122 data_t element5 = A[i+4];
123 data_t element6 = A[i+5];
124 data_t element7 = A[i+6];
125 data_t element8 = A[i+7];
126 int row= (int)(i/32)*32;
127 int column = i%32*32;
128 int column2 = (i+1)%32*32;
129 int column3 = (i+2)%32*32;
130 int column4 = (i+3)%32*32;
131 int column5 = (i+4)%32*32;
132 int column6 = (i+5)%32*32;
133 int column7 = (i+6)%32*32;
134 int column8 = (i+7)%32*32;
136 for (j=0; j<32; j++){
137 temp_mat[j]+=element*B[column+j]+element2*B[column2+j]+element3*B[column3+j]+element4*B[column4+j]+element5*B[column5+j]+element6*B[column6+j]+element7*B[column7+j]+element8*B[column8+j];
141 C[row+k]=temp_mat[k];
147 //data_t element11, element12, element13, element14, element21, element22, element23, element24;
148 data_t element1
, element2
, element3
, element4
, element5
, element6
, element7
, element8
;
150 //int column11, column12, column13, column14, column21, column22, column23, column24;
151 int column1
, column2
, column3
, column4
, column5
, column6
, column7
, column8
;
152 data_t temp
[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};
153 //data_t temp2[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};
155 for (i
=0; i
<32; i
++){
157 for (j
=0; j
<32; j
+=4){
160 element2
= A
[row
+j
+1];
161 element3
= A
[row
+j
+2];
162 element4
= A
[row
+j
+3];
168 temp
[k
]+=element1
*B
[column1
+k
]+element2
*B
[column2
+k
]+element3
*B
[column3
+k
]+element4
*B
[column4
+k
];
171 for (l
=0; l
<32; l
++){
180 for (j
=0; j
<16; j
+=4){
181 element1
= A
[i
*32+j
];
182 element2
= A
[i
*32+j
+1];
183 element3
= A
[i
*32+j
+2];
184 element4
= A
[i
*32+j
+3];
189 for (k
=0; k
<32; k
++){
190 temp
[k
]+=element1
*B
[column1
+k
]+element2
*B
[column2
+k
]+element3
*B
[column3
+k
]+element4
*B
[column4
+k
];
193 for (l
=0; l
<32; l
++){
203 for (i
=0; i
<32; i
++){
206 for (j
=16; j
<32; j
+=4){
207 element1
= A
[(31-i
)*32+j
];
208 element2
= A
[(31-i
)*32+j
+1];
209 element3
= A
[(31-i
)*32+j
+2];
210 element4
= A
[(31-i
)*32+j
+3];
215 for (k
=0; k
<32; k
++){
216 temp
[k
]+=element1
*B
[column1
+k
]+element2
*B
[column2
+k
]+element3
*B
[column3
+k
]+element4
*B
[column4
+k
];
219 for (l
=0; l
<32; l
++){
228 // ***************************** //
229 // **** ADD YOUR CODE HERE ***** //
230 // ***************************** //
232 // feel free to make a separate function for MI and MSI versions.
236 //--------------------------------------------------------------------------
239 // all threads start executing thread_entry(). Use their "coreid" to
240 // differentiate between threads (each thread is running on a separate core).
242 void thread_entry(int cid
, int nc
)
247 // static allocates data in the binary, which is visible to both threads
248 static data_t results_data
[ARRAY_SIZE
];
251 // Execute the provided, naive matmul
253 stats(matmul_naive(DIM_SIZE
, input1_data
, input2_data
, results_data
); barrier(nc
));
257 verifyMT(ARRAY_SIZE
, results_data
, verify_data
);
259 // clear results from the first trial
262 for (i
=0; i
< ARRAY_SIZE
; i
++)
267 // Execute your faster matmul
269 stats(matmul(DIM_SIZE
, input1_data
, input2_data
, results_data
); barrier(nc
));
272 printArrayMT("results:", ARRAY_SIZE
, results_data
);
273 printArrayMT("verify :", ARRAY_SIZE
, verify_data
);
277 verifyMT(ARRAY_SIZE
, results_data
, verify_data
);