[pytorch] Invoke vector.reserve() consistently for non-inplace foreach operations#161128
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tsunghsienlee wants to merge 1 commit intopytorch:mainfrom
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[pytorch] Invoke vector.reserve() consistently for non-inplace foreach operations#161128tsunghsienlee wants to merge 1 commit intopytorch:mainfrom
vector.reserve() consistently for non-inplace foreach operations#161128tsunghsienlee wants to merge 1 commit intopytorch:mainfrom
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…ach operations Summary: The `reserve()` method is used to pre-allocate memory for the result vector before adding elements to it. This is an optimization that makes sense for several reasons: 1. Performance improvement: By pre-allocating memory for the exact number of elements needed, it avoids multiple reallocations and memory copies that would occur as the vector grows dynamically. 2. Memory efficiency: It ensures that the vector allocates exactly the amount of memory needed, no more and no less, which is efficient when we know the final size in advance. 3. Reduced overhead: Each reallocation typically involves: - Allocating a new, larger block of memory - Copying all existing elements to the new location - Destroying the old elements - Deallocating the old memory block - Consistent performance: Without reservation, vector growth typically follows a geometric progression (like 1, 2, 4, 8, 16...), which can lead to unpredictable performance spikes when reallocation occurs. Test Plan: OSS CI & tests Rollback Plan: Differential Revision: D80674453
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/161128
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…ach operations (pytorch#161128) Summary: The `reserve()` method is used to pre-allocate memory for the result vector before adding elements to it. This is an optimization that makes sense for several reasons: 1. Performance improvement: By pre-allocating memory for the exact number of elements needed, it avoids multiple reallocations and memory copies that would occur as the vector grows dynamically. 2. Memory efficiency: It ensures that the vector allocates exactly the amount of memory needed, no more and no less, which is efficient when we know the final size in advance. 3. Reduced overhead: Each reallocation typically involves: - Allocating a new, larger block of memory - Copying all existing elements to the new location - Destroying the old elements - Deallocating the old memory block - Consistent performance: Without reservation, vector growth typically follows a geometric progression (like 1, 2, 4, 8, 16...), which can lead to unpredictable performance spikes when reallocation occurs. Test Plan: OSS CI & tests Rollback Plan: Differential Revision: D80674453 Pull Request resolved: pytorch#161128 Approved by: https://github.com/Skylion007
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Summary:
The
reserve()method is used to pre-allocate memory for the result vector before adding elements to it. This is an optimization that makes sense for several reasons:Performance improvement: By pre-allocating memory for the exact number of elements needed, it avoids multiple reallocations and memory copies that would occur as the vector grows dynamically.
Memory efficiency: It ensures that the vector allocates exactly the amount of memory needed, no more and no less, which is efficient when we know the final size in advance.
Reduced overhead: Each reallocation typically involves:
Test Plan:
OSS CI & tests
Rollback Plan:
Differential Revision: D80674453