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gpu: nvgpu: Fix Division by zero defect

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Fix following Coverity Defect:
profile.c : Division or modulo by zero

CID 10061399

Bug 3460991

Signed-off-by: Jinesh Parakh <jparakh@nvidia.com>
Change-Id: I03979af4ab105f659cf0fe3eac8d21946dfca950
Reviewed-on: https://git-master.nvidia.com/r/c/linux-nvgpu/+/2695362
Reviewed-by: svc-mobile-coverity <svc-mobile-coverity@nvidia.com>
Reviewed-by: svc-mobile-cert <svc-mobile-cert@nvidia.com>
Reviewed-by: svc-mobile-misra <svc-mobile-misra@nvidia.com>
Reviewed-by: Sagar Kamble <skamble@nvidia.com>
Reviewed-by: Sachin Nikam <snikam@nvidia.com>
GVS: Gerrit_Virtual_Submit
This commit is contained in:
Jinesh Parakh
2022-04-11 11:42:39 +05:30
committed by mobile promotions
parent 5b38f48227
commit 131933d528
1 changed files with 28 additions and 9 deletions
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37
drivers/gpu/nvgpu/common/swdebug/profile.c
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@@ -447,17 +447,33 @@ static u32 nvgpu_swprofile_subsample_basic_stats(struct gk20a *g,
/* With the sorted list of samples we can easily compute the median. */ /* With the sorted list of samples we can easily compute the median. */
sort(storage, samples, sizeof(u64), profile_cmp, NULL); sort(storage, samples, sizeof(u64), profile_cmp, NULL);
if (samples == 0U) {
return 0U;
}
mean = sum / samples; mean = sum / samples;
median = storage[samples / 2]; median = storage[samples / 2];
/* Compute the sample variance (i.e sigma squared). */ /*
for (i = 0U; i < samples; i++) { * If only 1 sample is found, the min, max, median and mean would be
sigma_2 += storage[i] * storage[i]; * the value of that one observation (storage[0]). The variance in this
} * case would be 0.
* The need for this special case is because in the original implementation,
* sigma_2 is divided by samples-1 which is 0 in our case, causing a divide
* by zero error.
*/
if (samples == 1U) {
sigma_2 = 0U;
} else {
/* Compute the sample variance (i.e sigma squared). */
for (i = 0U; i < samples; i++) {
sigma_2 += storage[i] * storage[i];
}
/* Remember: _sample_ variance. */ /* Remember: _sample_ variance. */
sigma_2 /= (samples - 1U); sigma_2 /= (samples - 1U);
sigma_2 -= (mean * mean); sigma_2 -= (mean * mean);
}
results[0] = min; results[0] = min;
results[1] = max; results[1] = max;
@@ -509,9 +525,12 @@ void nvgpu_swprofile_print_basic_stats(struct gk20a *g,
samples = nvgpu_swprofile_subsample_basic_stats(g, p, i, samples = nvgpu_swprofile_subsample_basic_stats(g, p, i,
results, storage); results, storage);
if (samples == 0U) {
continue;
}
gk20a_debug_output(o, fmt_output, p->col_names[i], gk20a_debug_output(o, fmt_output, p->col_names[i],
results[0], results[1], results[0], results[1],
results[2], results[3], results[4]); results[2], results[3], results[4]);
} }
gk20a_debug_output(o, "Number of samples: %u\n", samples); gk20a_debug_output(o, "Number of samples: %u\n", samples);