I need to find the largest element in an array that contains exactly 16 integers. I'm considering two possible implementations. First, the sensible implementation:
int largest = array[0];
for (int i = 1; i < 16; i++) {
const int val = array[i];
if (val > largest) {
largest = val;
}
}
And then there's the slightly insane implementation that takes advantage of the fact that the size of the array is known:
const int max_value =
max(
max(
max(
max(array[0], array[1]),
max(array[2], array[3])),
max(
max(array[4], array[5]),
max(array[6], array[7]))),
max(
max(
max(array[8], array[9])
max(array[10], array[11])),
max(
max(array[12], array[13])
max(array[14], array[15]))));
Which is the better implementation? Is max
typically implemented in hardware?
To find the largest element from the array, a simple way is to arrange the elements in ascending order. After sorting, the first element will represent the smallest element, the next element will be the second smallest, and going on, the last element will be the largest element of the array.
Initialize max with the first element initially, to start the comparison. Then traverse the given array from second element till end, and for each element: Compare the current element with max. If the current element is greater than max, then replace the value of max with the current element.
max(). The max() method of java. util. Collections class is used to return the maximum element of the given collection, according to the natural ordering of its elements.
Let's compile them and see what we get!
First of all, AFAIK, there is no "max" function/macro defined in the C standard. So I added one (which looks convoluted because it avoids double evaluation of its inputs).
#define max(a,b) ({ \
const __typeof__ (a) _a = (a); \
const __typeof__ (b) _b = (b); \
_a > _b ? _a : _b; \
})
int __attribute__ ((noinline)) test1(const int* array) {
int largest = array[0];
for (int i = 1; i < 16; i++) {
const int val = array[i];
if (val > largest) {
largest = val;
}
}
return largest;
}
int __attribute__ ((noinline)) test2(const int* array) {
const int max_value =
max(
max(
max(
max(array[0], array[1]),
max(array[2], array[3])),
max(
max(array[4], array[5]),
max(array[6], array[7]))),
max(
max(
max(array[8], array[9]),
max(array[10], array[11])),
max(
max(array[12], array[13]),
max(array[14], array[15]))));
return max_value;
}
My gcc version, which is relevant when speaking about optimizations:
tmp$ gcc --version
gcc (Ubuntu 4.8.4-2ubuntu1~14.04) 4.8.4
Copyright (C) 2013 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-O2
for optimizations, -S
to output assembly, -o -
to output to stdout.
tmp$ gcc -std=c99 -O2 -S test.c -o -
.file "test.c"
.text
.p2align 4,,15
.globl test1
.type test1, @function
test1:
.LFB0:
.cfi_startproc
movl (%rdi), %eax
xorl %edx, %edx
.p2align 4,,10
.p2align 3
.L3:
movl 4(%rdi,%rdx), %ecx
cmpl %ecx, %eax
cmovl %ecx, %eax
addq $4, %rdx
cmpq $60, %rdx
jne .L3
rep ret
.cfi_endproc
.LFE0:
.size test1, .-test1
.p2align 4,,15
.globl test2
.type test2, @function
test2:
.LFB1:
.cfi_startproc
movl (%rdi), %edx
cmpl %edx, 4(%rdi)
cmovge 4(%rdi), %edx
movl 8(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 12(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 16(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 20(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 24(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 28(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 32(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 36(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 40(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 44(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 48(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 52(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 56(%rdi), %eax
cmpl %eax, %edx
cmovl %eax, %edx
movl 60(%rdi), %eax
cmpl %eax, %edx
cmovge %edx, %eax
ret
.cfi_endproc
.LFE1:
.size test2, .-test2
.ident "GCC: (Ubuntu 4.8.4-2ubuntu1~14.04) 4.8.4"
.section .note.GNU-stack,"",@progbits
Okay, so test2()
certainly looks much longer. However, it doesn't branch at all. And it's only ~3 instructions (memory load, comparison, conditional move) per element. test1()
has 6 instructions (memory load, comparison, conditional move, loop counter increment, loop counter comparison, conditional branch). Lots of branches in test1
, which can be bothersome (depending on how good your architecture's branch prediction is). On the other hand, test2
increases code size, which will necessarily push something else out of instruction cache. And there are lots of data hazards in test2
(well, and test1
...) -- maybe we could rewrite it to use a few extra registers to reduce the number of pipeline stalls?
So, as you can probably see by now, this is not an easy question to answer.
The only real way to know is to measure it. And even then, it'll vary depending on internal implementation/optimizations/cache size of each CPU model.
So I wrote a small benchmark:
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <stdint.h>
#define N (1000000)
int main() {
printf(" %12s %12s %12s %12s\n", "test1 time", "test2 time", "test1 out", "test2 out");
int* data = malloc(N * 16 * sizeof(int));
srand(1);
for (int i=0; i<16*N; ++i) {
data[i] = rand();
}
const int* a;
struct timespec t1, t2, t3;
for (int attempt=0; attempt<10; ++attempt) {
uint32_t sum1 = 0;
uint32_t sum2 = 0;
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &t1);
a = data;
for (int i=0; i<N; ++i) {
sum1 += test1(a);
a += 16;
}
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &t2);
a = data;
for (int i=0; i<N; ++i) {
sum2 += test2(a);
a += 16;
}
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &t3);
uint64_t nanos1 = (t2.tv_sec - t1.tv_sec) * 1000000000L + (t2.tv_nsec - t1.tv_nsec);
uint64_t nanos2 = (t3.tv_sec - t2.tv_sec) * 1000000000L + (t3.tv_nsec - t2.tv_nsec);
printf("%2d: %12lu %12lu %12u %12u\n", attempt+1, nanos1, nanos2, sum1, sum2);
}
return 0;
}
And the results:
tmp$ gcc -std=gnu99 -O2 test.c -o test
tmp$ ./test
test1 time test2 time test1 out test2 out
1: 16251659 10431322 4190722540 4190722540
2: 16796884 10639081 4190722540 4190722540
3: 16443265 10314624 4190722540 4190722540
4: 17194795 10337678 4190722540 4190722540
5: 16966405 10380047 4190722540 4190722540
6: 16803840 10556222 4190722540 4190722540
7: 16795989 10871508 4190722540 4190722540
8: 16389862 11511950 4190722540 4190722540
9: 16304850 11704787 4190722540 4190722540
10: 16309371 11269446 4190722540 4190722540
tmp$ gcc -std=gnu99 -O3 test.c -o test
tmp$ ./test
test1 time test2 time test1 out test2 out
1: 9090364 8813462 4190722540 4190722540
2: 8745093 9394730 4190722540 4190722540
3: 8942015 9839356 4190722540 4190722540
4: 8849960 8834056 4190722540 4190722540
5: 9567597 9195950 4190722540 4190722540
6: 9130245 9115883 4190722540 4190722540
7: 9680596 8930225 4190722540 4190722540
8: 9268440 9998824 4190722540 4190722540
9: 8851503 8960392 4190722540 4190722540
10: 9767021 8875165 4190722540 4190722540
tmp$ gcc -std=gnu99 -Os test.c -o test
tmp$ ./test
test1 time test2 time test1 out test2 out
1: 17569606 10447512 4190722540 4190722540
2: 17755450 10811861 4190722540 4190722540
3: 17718714 10372411 4190722540 4190722540
4: 17743248 10378728 4190722540 4190722540
5: 18747440 10306748 4190722540 4190722540
6: 17877105 10782263 4190722540 4190722540
7: 17787171 10522498 4190722540 4190722540
8: 17771172 10445461 4190722540 4190722540
9: 17683935 10430900 4190722540 4190722540
10: 17670540 10543926 4190722540 4190722540
tmp$ gcc -std=gnu99 -O2 -funroll-loops test.c -o test
tmp$ ./test
test1 time test2 time test1 out test2 out
1: 9840366 10008656 4190722540 4190722540
2: 9826522 10529205 4190722540 4190722540
3: 10208039 10363219 4190722540 4190722540
4: 9863467 10284608 4190722540 4190722540
5: 10473329 10054511 4190722540 4190722540
6: 10298968 10520570 4190722540 4190722540
7: 9846157 10595723 4190722540 4190722540
8: 10340026 10041021 4190722540 4190722540
9: 10434750 10404669 4190722540 4190722540
10: 9982403 10592842 4190722540 4190722540
Conclusion: the max() version is faster on my Intel Core i7-3517U with 4 MB cache (and I wouldn't claim any more than that because, again, the results may vary with microarchitecture).
Also, -funroll-loops
or the extra-aggressive (and less safe) optimizations enabled by -O3
really make a huge difference for the test1
case, essentially making it equal in time to test2
-- perhaps even slightly better with -funroll-loops
, but close enough that we can't draw a confident conclusion from the numbers I got. It would probably be interesting to look at the assembly for test1
there, but I'll leave that as an exercise for the reader. ;)
So, I guess the answer is "it depends".
The first one is clearly the most straightforward implementation.
Nonetheless, this problem is related to the concept of Sorting Networks, which is a surprisingly complex theory on sorting fixed-size sets of data.
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