I know more c++than c#(spent X years full time)

Action: spend enough (30?) hours revisiting my 1000+ cpp (including socket, stream..) blog posts and explore just a little beyond them.

(I blogged about exactly the same observation before …) Here’s a paradox — i spent 2 years in a full time c# job, arguably longer than my full time c++ experience. However, sometimes I feel I understand dotnet less than C++.

Reason? Many aspects of the languages are never used in-depth on work projects, so length of experience != depth of experience. We need spare time self-exploration to learn those aspects like:

– template meta programming
– custom operator new/delete
– memory tuning
– memory leak detection
– threading
– async communications
– pure virtual
– ++i vs i++

(… not a complete list by any measure.) Interviewers are notorious for testing these obscure or advanced, theoretical topics.

##10basic constructs4c++cod`IV

See EPI300

  1. std::string (more useful than cStr)
  2. vector (more useful than array) sorted data structure (i.e. stl map), unordered_map
  3. Node class used in a linked graph
  4. dtor, copier, op=
  5. ref only as function I/O
  6. iterator – basic usage
  7. double pointer
  8. stack, queue
  9. pointer arithmetic
  10. shared_ptr
  11. local static

no exception
stl algo? Only Citadel array-shrink
no pointer to function
no template
no (very, very seldom) threading in coding Q
adv: matrix
adv: circular buffer


my quicksort in python/c++

# partition the given section by fixing the anchor
def partition(A, le, ri):
    pivot = A[ri] # immutable
    pivotPos = i = ri
    while i > le:
        i -= 1
        if A[i] <= pivot: continue swap(A, pivotPos-1, i) swap(A, pivotPos-1, pivotPos) #print 'after swap ...', A[le:ri+1] pivotPos -= 1 return pivotPos def recurse(A, le, ri): if le>=ri: return
    print 'entering partition()...',
    print(A[le:ri+1]), ' pivotVal =', A[ri]
    anchor = partition(A, le, ri)
    print '...after partition()   ',
    recurse(A, le, anchor-1)
    recurse(A, anchor+1, ri)
def swap(A, x,y):
    tmp = A[x]
    A[x] = A[y]
    A[y] = tmp
def qsort(A):
    recurse(A, 0, len(A)-1)


Above is py, below is c++


#include <iostream>
#include <vector>

std::vector<int> A{77, 11, 66,22,33,99,44,88, 77, 55, 0};
int const size = A.size();

void dump(int l=0, int r=size-1) {
	for (int i = l; i <= r; ++i)
		std::cout << A[i] << ' ';
	std::cout <<std::endl;

template <typename T>
void swap(int pos1, int pos2) {
	if (A[pos1] == A[pos2]) return;
	T tmp = A[pos1];
	A[pos1] = A[pos2];
	A[pos2] = tmp;

/*partition the region [l,r] such that all elements smaller than
pivotValue are on the left of pivotPosition
template <typename T>
int partitionUsing1stInefficient(int l, int r) {
	T const pivotVal = A[l];
	int pivotPos = l;
	for (int i = l+ 1; i <= r; ++i) { if (A[i] >= pivotVal) continue;
		swap<int>(pivotPos + 1, i);
		swap<int>(pivotPos + 1, pivotPos);
	return pivotPos;
template <typename T>
int partitionUsingLastInefficient(int l, int r) {
	T const pivotVal = A[r];
	int pivotPos = r;
	for (int i = r - 1; i >= l; --i) {
		if (A[i] <= pivotVal) continue;
		swap<int>(pivotPos - 1, i);
		swap<int>(pivotPos - 1, pivotPos);
	return pivotPos;
/*based on [[Algorithms unlocked]]
Lime zone -- items smaller than pivot value
Red zone -- items bigger than pivot value
Ugly zone -- items yet to be checked
template <typename T>
int partitionMinimalSwap(int le, int ri) {
	T const pivotVal = A[ri];
	// two boundaries exist between zones
	int redStart = le;
	// we start with redStart == uglyStart == l, which means item at le is Unknown
	for (int uglyStart = le; uglyStart < ri; ++uglyStart) {
		if (A[uglyStart] < pivotVal) {
			swap<int>(uglyStart, redStart);
	swap<int>(ri, redStart);
	return redStart;

template <typename T>
void recurse(int l, int r) {
	if (l >= r) return; // recursion exit condition
	int const anchor = partitionMinimalSwap<T>(l, r);
	recurse<T>(l, anchor-1);
	recurse<T>(anchor+1, r);

int main() {
	recurse<int>(0, size-1);
	return 0;

threading – I/O^CPU intensive

See also — [[linux sys programming]] has an one-pager on this topic.
This is a common topic in IV and in the literature. HSBC market data + algo trading IV 2017 also touched on this.
IO-intensive – may scale up to hundreds of threads, even with just 4 cores. Each thread handles some I/O channel or connection.
eg(?): network server
eg: GUI – multiple threads could be waiting for disk or user input
CPU-intensive on multi-core machines – don’t need too many threads, but single-thread is non-optimal. Each core is effectively dedicated to a single thread.