Lecture 7 - Simple parallel algorithms

Data-parallel vs task-parallel:

• data-parallel — same operation is performed, independently, on distinct subsets of the processing data;
• task-parallel — distinct operations, that do not depend on each other, are performed in parallel

The most general approach: consider the dependency graph between computed quantities. It is a DAG (directed acyclic graph). Notes:

• parallelizable activities can be found, for instance, by splitting the graph into levels;
• the critical path (the longest path, or the maximum cost if distinct processing steps take different times) gives a lower limit for the execution time even on an infinite number of CPUs.
• sometimes, the critical path can be shorteden at the expense of increasing the amount of computation to be performed.
• the graph may not be known from the beginning...

Simple data decomposition

Processing of an array of data can often be split into independent blocks.

The easiest case is when each input produces one output — the map pattern. See the example for computing the sum of two vectors: vector_sum_split_work.cpp.

Simple way of computing the boundary index: beginIdx = (threadIdx * nrElements) div nrThreads

However, beware of cache effects! Processing consecutive elements is significantly faster than processing every k-th element. Compare the previous program with the one at vector_sum_split_work_bad.cpp.

A more complex case arises when each output depends on a group of inputs, around the input at the same position — the stencil pattern. See vector_average_stencil.cpp.

It is preferrable to split on output than on inputs — so that each output is computed by exactly one worker (thread, task) and so no mutexes are necessary.

Recursive decomposition

The initial worker splits the data into two or more fragments, gives the fragments as inputs to subordinate workers, and finally it combines the results.

Example 1: Compute the sum of a vector. Create a binary tree of adders. The depth is O(log(n). Source code: recursive_decomposition_sum.cpp.

Example 2: Merge sort. The basic (non-parallel algorith) is to divide the input vector into two parts, merge-sort each part, then merge the resulting two sorted vectors into one. For parallelizing, merge-sorting the two parts can easily be done in parallel. However, the final merge is a bit harder. It can be done as follows:

• take the middle element in the first sorted vector;
• find its position in the second sorted vector, by a binary search;
• divide the two vectors by the two positions found above;
• merge independently the two pairs of sub-vectors;
• concatenate the results (this is a no-op actually).

See the C++ implementations:

• mergesort.cpp — serial implementation;
• mergesort-par1.cpp — parallelized, but with non-parallel merge;
• mergesort-par2.cpp — fully parallelized, including parallel merge;

Example 3: Compute the sequence of sums of prefixes. Given a₀, a₁, ..., a_n-1, compute b₀ = a₀, b₁ = a₀+a₁, b₂ = a₀+a₁+a₂,..., b_n-1 = a₀+a₁+a₂+...+a_n-1.

Solution: start with a binary tree computing the sum of all numbers in the sequence. Then, compute each prefix sum from the largest parts already computed.

  // First, compute the sums of 2^j consecutive numbers;
  // b[i*2^j - 1] = a[(i-1)*2^j] + ... + a[(i-1)*2^j + 2^j - 1]
  b = a
  for(size_t k=1 ; k<n ; k = k*2) {
      for(size_t i=2*k-1 ; i<n ; i+=2*k) { // in parallel
          b[i] += b[i-k];
      }
  }
  // Then, compute each partial sum as a sum of 2^j groups:
  k = k/4
  for( ; k>0 ; k = k/2) {
      for(size_t i=3*k-1 ; i<n ; i+=2*k) { // in parallel
          b[i] += b[i-k];
      }
  }

Examples: