Switch: _GLIBCXX_PROFILE_HASHTABLE_TOO_SMALL.

Goal: Detect hashtables with many rehash operations, small construction size and large destruction size.

Fundamentals: Rehash is very expensive. Read content, follow chains within bucket, evaluate hash function, place at new location in different order.

Sample runtime reduction: 36%. Code similar to example below.

Recommendation: Set initial size to N at construction site S.

To instrument: unordered_set, unordered_map constructor, destructor, rehash.

Analysis: For each dynamic instance of unordered_[multi]set|map, record initial size and call context of the constructor. Record size increase, if any, after each relevant operation such as insert. Record the estimated rehash cost.

Cost model: Number of individual rehash operations * cost per rehash.

Example:

1 unordered_set<int> us;
2 for (int k = 0; k < 1000000; ++k) {
3   us.insert(k);
4 }

foo.cc:1: advice: Changing initial unordered_set size from 10 to 1000000 saves 1025530 rehash operations.

Switch: _GLIBCXX_PROFILE_HASHTABLE_TOO_LARGE.

Goal: Detect hashtables which are never filled up because fewer elements than reserved are ever inserted.

Fundamentals: Save memory, which is good in itself and may also improve memory reference performance through fewer cache and TLB misses.

Sample runtime reduction: unknown.

Recommendation: Set initial size to N at construction site S.

To instrument: unordered_set, unordered_map constructor, destructor, rehash.

Analysis: For each dynamic instance of unordered_[multi]set|map, record initial size and call context of the constructor, and correlate it with its size at destruction time.

Cost model: Number of iteration operations + memory saved.

Example:

1 vector<unordered_set<int>> v(100000, unordered_set<int>(100)) ;
2 for (int k = 0; k < 100000; ++k) {
3   for (int j = 0; j < 10; ++j) {
4     v[k].insert(k + j);
5  }
6 }

foo.cc:1: advice: Changing initial unordered_set size from 100 to 10 saves N
bytes of memory and M iteration steps.

Switch: _GLIBCXX_PROFILE_INEFFICIENT_HASH.

Goal: Detect hashtables with polarized distribution.

Fundamentals: A non-uniform distribution may lead to long chains, thus possibly increasing complexity by a factor up to the number of elements.

Sample runtime reduction: factor up to container size.

Recommendation: Change hash function for container built at site S. Distribution score = N. Access score = S. Longest chain = C, in bucket B.

To instrument: unordered_set, unordered_map constructor, destructor, [], insert, iterator.

Analysis: Count the exact number of link traversals.

Cost model: Total number of links traversed.

Example:

class dumb_hash {
 public:
  size_t operator() (int i) const { return 0; }
};
...
  unordered_set<int, dumb_hash> hs;
  ...
  for (int i = 0; i < COUNT; ++i) {
    hs.find(i);
  }

Switch: _GLIBCXX_PROFILE_VECTOR_TOO_SMALL.

Goal:Detect vectors with many resize operations, small construction size and large destruction size..

Fundamentals:Resizing can be expensive. Copying large amounts of data takes time. Resizing many small vectors may have allocation overhead and affect locality.

Sample runtime reduction:%.

Recommendation: Set initial size to N at construction site S.

To instrument:vector.

Analysis: For each dynamic instance of vector, record initial size and call context of the constructor. Record size increase, if any, after each relevant operation such as push_back. Record the estimated resize cost.

Cost model: Total number of words copied * time to copy a word.

Example:

1 vector<int> v;
2 for (int k = 0; k < 1000000; ++k) {
3   v.push_back(k);
4 }

foo.cc:1: advice: Changing initial vector size from 10 to 1000000 saves
copying 4000000 bytes and 20 memory allocations and deallocations.

Switch: _GLIBCXX_PROFILE_VECTOR_TOO_LARGE

Goal:Detect vectors which are never filled up because fewer elements than reserved are ever inserted.

Fundamentals:Save memory, which is good in itself and may also improve memory reference performance through fewer cache and TLB misses.

Sample runtime reduction:%.

Recommendation: Set initial size to N at construction site S.

To instrument:vector.

Analysis: For each dynamic instance of vector, record initial size and call context of the constructor, and correlate it with its size at destruction time.

Cost model: Total amount of memory saved.

Example:

1 vector<vector<int>> v(100000, vector<int>(100)) ;
2 for (int k = 0; k < 100000; ++k) {
3   for (int j = 0; j < 10; ++j) {
4     v[k].insert(k + j);
5  }
6 }

foo.cc:1: advice: Changing initial vector size from 100 to 10 saves N
bytes of memory and may reduce the number of cache and TLB misses.

Switch: _GLIBCXX_PROFILE_VECTOR_TO_HASHTABLE.

Goal: Detect uses of vector that can be substituted with unordered_set to reduce execution time.

Fundamentals: Linear search in a vector is very expensive, whereas searching in a hashtable is very quick.

Sample runtime reduction:factor up to container size.

Recommendation:Replace vector with unordered_set at site S.

To instrument:vector operations and access methods.

Analysis: For each dynamic instance of vector, record call context of the constructor. Issue the advice only if the only methods called on this vector are push_back, insert and find.

Cost model: Cost(vector::push_back) + cost(vector::insert) + cost(find, vector) - cost(unordered_set::insert) + cost(unordered_set::find).

Example:

1  vector<int> v;
...
2  for (int i = 0; i < 1000; ++i) {
3    find(v.begin(), v.end(), i);
4  }

foo.cc:1: advice: Changing "vector" to "unordered_set" will save about 500,000
comparisons.

Switch: _GLIBCXX_PROFILE_HASHTABLE_TO_VECTOR.

Goal: Detect uses of unordered_set that can be substituted with vector to reduce execution time.

Fundamentals: Hashtable iterator is slower than vector iterator.

Sample runtime reduction:95%.

Recommendation:Replace unordered_set with vector at site S.

To instrument:unordered_set operations and access methods.

Analysis: For each dynamic instance of unordered_set, record call context of the constructor. Issue the advice only if the number of find, insert and [] operations on this unordered_set are small relative to the number of elements, and methods begin or end are invoked (suggesting iteration).

Cost model: Number of .

Example:

1  unordered_set<int> us;
...
2  int s = 0;
3  for (unordered_set<int>::iterator it = us.begin(); it != us.end(); ++it) {
4    s += *it;
5  }

foo.cc:1: advice: Changing "unordered_set" to "vector" will save about N
indirections and may achieve better data locality.

Switch: _GLIBCXX_PROFILE_VECTOR_TO_LIST.

Goal: Detect cases where vector could be substituted with list for better performance.

Fundamentals: Inserting in the middle of a vector is expensive compared to inserting in a list.

Sample runtime reduction:factor up to container size.

Recommendation:Replace vector with list at site S.

To instrument:vector operations and access methods.

Analysis: For each dynamic instance of vector, record the call context of the constructor. Record the overhead of each insert operation based on current size and insert position. Report instance with high insertion overhead.

Cost model: (Sum(cost(vector::method)) - Sum(cost(list::method)), for method in [push_back, insert, erase]) + (Cost(iterate vector) - Cost(iterate list))

Example:

1  vector<int> v;
2  for (int i = 0; i < 10000; ++i) {
3    v.insert(v.begin(), i);
4  }

foo.cc:1: advice: Changing "vector" to "list" will save about 5,000,000
operations.

Switch: _GLIBCXX_PROFILE_LIST_TO_VECTOR.

Goal: Detect cases where list could be substituted with vector for better performance.

Fundamentals: Iterating through a vector is faster than through a list.

Sample runtime reduction:64%.

Recommendation:Replace list with vector at site S.

To instrument:vector operations and access methods.

Analysis: Issue the advice if there are no insert operations.

Cost model: (Sum(cost(vector::method)) - Sum(cost(list::method)), for method in [push_back, insert, erase]) + (Cost(iterate vector) - Cost(iterate list))

Example:

1  list<int> l;
...
2  int sum = 0;
3  for (list<int>::iterator it = l.begin(); it != l.end(); ++it) {
4    sum += *it;
5  }

foo.cc:1: advice: Changing "list" to "vector" will save about 1000000 indirect
memory references.

Switch: _GLIBCXX_PROFILE_LIST_TO_SLIST.

Goal: Detect cases where list could be substituted with forward_list for better performance.

Fundamentals: The memory footprint of a forward_list is smaller than that of a list. This has beneficial effects on memory subsystem, e.g., fewer cache misses.

Sample runtime reduction:40%. Note that the reduction is only noticeable if the size of the forward_list node is in fact larger than that of the list node. For memory allocators with size classes, you will only notice an effect when the two node sizes belong to different allocator size classes.

Recommendation:Replace list with forward_list at site S.

To instrument:list operations and iteration methods.

Analysis: Issue the advice if there are no backwards traversals or insertion before a given node.

Cost model: Always true.

Example:

1  list<int> l;
...
2  int sum = 0;
3  for (list<int>::iterator it = l.begin(); it != l.end(); ++it) {
4    sum += *it;
5  }

foo.cc:1: advice: Change "list" to "forward_list".

Switch: _GLIBCXX_PROFILE_ORDERED_TO_UNORDERED.

Goal: Detect cases where ordered associative containers can be replaced with unordered ones.

Fundamentals: Insert and search are quicker in a hashtable than in a red-black tree.

Sample runtime reduction:52%.

Recommendation: Replace set with unordered_set at site S.

To instrument: set, multiset, map, multimap methods.

Analysis: Issue the advice only if we are not using operator ++ on any iterator on a particular [multi]set|map.

Cost model: (Sum(cost(hashtable::method)) - Sum(cost(rbtree::method)), for method in [insert, erase, find]) + (Cost(iterate hashtable) - Cost(iterate rbtree))

Example:

1  set<int> s;
2  for (int i = 0; i < 100000; ++i) {
3    s.insert(i);
4  }
5  int sum = 0;
6  for (int i = 0; i < 100000; ++i) {
7    sum += *s.find(i);
8  }

Switch: _GLIBCXX_PROFILE_SORT.

Goal: Give measure of sort algorithm performance based on actual input. For instance, advise Radix Sort over Quick Sort for a particular call context.

Fundamentals: See papers: A framework for adaptive algorithm selection in STAPL and Optimizing Sorting with Machine Learning Algorithms.

Sample runtime reduction:60%.

Recommendation: Change sort algorithm at site S from X Sort to Y Sort.

To instrument: sort algorithm.

Analysis: Issue the advice if the cost model tells us that another sort algorithm would do better on this input. Requires us to know what algorithm we are using in our sort implementation in release mode.

Cost model: Runtime(algo) for algo in [radix, quick, merge, ...]

Example:

Switch: _GLIBCXX_PROFILE_SOFTWARE_PREFETCH.

Goal: Discover sequences of indirect memory accesses that are not regular, thus cannot be predicted by hardware prefetchers.

Fundamentals: Indirect references are hard to predict and are very expensive when they miss in caches.

Sample runtime reduction:25%.

Recommendation: Insert prefetch instruction.

To instrument: Vector iterator and access operator [].

Analysis: First, get cache line size and page size from system. Then record iterator dereference sequences for which the value is a pointer. For each sequence within a container, issue a warning if successive pointer addresses are not within cache lines and do not form a linear pattern (otherwise they may be prefetched by hardware). If they also step across page boundaries, make the warning stronger.

The same analysis applies to containers other than vector. However, we cannot give the same advice for linked structures, such as list, as there is no random access to the n-th element. The user may still be able to benefit from this information, for instance by employing frays (user level light weight threads) to hide the latency of chasing pointers.

This analysis is a little oversimplified. A better cost model could be created by understanding the capability of the hardware prefetcher. This model could be trained automatically by running a set of synthetic cases.

Cost model: Total distance between pointer values of successive elements in vectors of pointers.

Example:

1 int zero = 0;
2 vector<int*> v(10000000, &zero);
3 for (int k = 0; k < 10000000; ++k) {
4   v[random() % 10000000] = new int(k);
5 }
6 for (int j = 0; j < 10000000; ++j) {
7   count += (*v[j] == 0 ? 0 : 1);
8 }

foo.cc:7: advice: Insert prefetch instruction.

Switch: _GLIBCXX_PROFILE_RBTREE_LOCALITY.

Goal: Give measure of locality of objects stored in linked structures (lists, red-black trees and hashtables) with respect to their actual traversal patterns.

Fundamentals:Allocation can be tuned to a specific traversal pattern, to result in better data locality. See paper: Custom Memory Allocation for Free.

Sample runtime reduction:30%.

Recommendation: High scatter score N for container built at site S. Consider changing allocation sequence or choosing a structure conscious allocator.

To instrument: Methods of all containers using linked structures.

Analysis: First, get cache line size and page size from system. Then record the number of successive elements that are on different line or page, for each traversal method such as find. Give advice only if the ratio between this number and the number of total node hops is above a threshold.

Cost model: Sum(same_cache_line(this,previous))

Example:

 1  set<int> s;
 2  for (int i = 0; i < 10000000; ++i) {
 3    s.insert(i);
 4  }
 5  set<int> s1, s2;
 6  for (int i = 0; i < 10000000; ++i) {
 7    s1.insert(i);
 8    s2.insert(i);
 9  }
...
      // Fast, better locality.
10    for (set<int>::iterator it = s.begin(); it != s.end(); ++it) {
11      sum += *it;
12    }
      // Slow, elements are further apart.
13    for (set<int>::iterator it = s1.begin(); it != s1.end(); ++it) {
14      sum += *it;
15    }

foo.cc:5: advice: High scatter score NNN for set built here.  Consider changing
the allocation sequence or switching to a structure conscious allocator.

Switch: _GLIBCXX_PROFILE_DDTEST.

Goal: Detect container elements that are referenced from multiple threads in the parallel region or across parallel regions.

Fundamentals: Sharing data between threads requires communication and perhaps locking, which may be expensive.

Sample runtime reduction:?%.

Recommendation: Change data distribution or parallel algorithm.

To instrument: Container access methods and iterators.

Analysis: Keep a shadow for each container. Record iterator dereferences and container member accesses. Issue advice for elements referenced by multiple threads. See paper: The LRPD test: speculative run-time parallelization of loops with privatization and reduction parallelization.

Cost model: Number of accesses to elements referenced from multiple threads

Example:

Switch: _GLIBCXX_PROFILE_FALSE_SHARING.

Goal: Detect elements in the same container which share a cache line, are written by at least one thread, and accessed by different threads.

Fundamentals: Under these assumptions, cache protocols require communication to invalidate lines, which may be expensive.

Sample runtime reduction:68%.

Recommendation: Reorganize container or use padding to avoid false sharing.

To instrument: Container access methods and iterators.

Analysis: First, get the cache line size. For each shared container, record all the associated iterator dereferences and member access methods with the thread id. Compare the address lists across threads to detect references in two different threads to the same cache line. Issue a warning only if the ratio to total references is significant. Do the same for iterator dereference values if they are pointers.

Cost model: Number of accesses to same cache line from different threads.

Example:

1     vector<int> v(2, 0);
2 #pragma omp parallel for shared(v, SIZE) schedule(static, 1)
3     for (i = 0; i < SIZE; ++i) {
4       v[i % 2] += i;
5     }

OMP_NUM_THREADS=2 ./a.out
foo.cc:1: advice: Change container structure or padding to avoid false
sharing in multithreaded access at foo.cc:4.  Detected N shared cache lines.