.. _Wavefront:
Wavefront
=========
.. container:: section
.. rubric:: Problem
:class: sectiontitle
Perform computations on items in a data set, where the computation on
an item uses results from computations on predecessor items.
.. container:: section
.. rubric:: Context
:class: sectiontitle
The dependences between computations form an acyclic graph.
.. container:: section
.. rubric:: Forces
:class: sectiontitle
- Dependence constraints between items form an acyclic graph.
- The number of immediate predecessors in the graph is known in
advance, or can be determined some time before the last
predecessor completes.
.. container:: section
.. rubric:: Solution
:class: sectiontitle
The solution is a parallel variant of topological sorting, using
``oneapi::tbb::parallel_for_each`` to process items. Associate an atomic
counter with each item. Initialize each counter to the number of
predecessors. Invoke ``oneapi::tbb::parallel_for_each`` to process the items that
have no predessors (have counts of zero). After an item is processed,
decrement the counters of its successors. If a successor's counter
reaches zero, add that successor to the ``oneapi::tbb::parallel_for_each``
via a "feeder".
If the number of predecessors for an item cannot be determined in
advance, treat the information "know number of predecessors" as an
additional predecessor. When the number of predecessors becomes
known, treat this conceptual predecessor as completed.
If the overhead of counting individual items is excessive, aggregate
items into blocks, and do the wavefront over the blocks.
.. container:: section
.. rubric:: Example
:class: sectiontitle
Below is a serial kernel for the longest common subsequence
algorithm. The parameters are strings ``x`` and ``y`` with respective
lengths ``xlen`` and ``ylen``.
::
int F[MAX_LEN+1][MAX_LEN+1];
void SerialLCS( const char* x, size_t xlen, const char* y, size_t ylen )
{
for( size_t i=1; i<=xlen; ++i )
for( size_t j=1; j<=ylen; ++j )
F[i][j] = x[i-1]==y[j-1] ? F[i-1][j-1]+1:
max(F[i][j-1],F[i-1][j]);
}
The kernel sets ``F[i][j]`` to the length of the longest common
subsequence shared by ``x[0..i-1]`` and ``y[0..j-1]``. It assumes
that F[0][0..ylen] and ``F[0..xlen][0]`` have already been
initialized to zero.
The following figure shows the data dependences for calculating
``F[i][j]``.
.. container:: fignone
:name: fig3
Data dependences for longest common substring calculation.
|image0|
The following figure shows the gray diagonal dependence is the
transitive closure of other dependencies. Thus for parallelization
purposes it is a redundant dependence that can be ignored.
.. container:: fignone
:name: fig4
Diagonal dependence is redundant.
|image1|
It is generally good to remove redundant dependences from
consideration, because the atomic counting incurs a cost for each
dependence considered.
Another consideration is grain size. Scheduling each ``F[i][j]``
element calculation separately is prohibitively expensive. A good
solution is to aggregate the elements into contiguous blocks, and
process the contents of a block serially. The blocks have the same
dependence pattern, but at a block scale. Hence scheduling overheads
can be amortized over blocks.
The parallel code follows. Each block consists of ``N×N`` elements.
Each block has an associated atomic counter. Array ``Count``
organizes these counters for easy lookup. The code initializes the
counters and then rolls a wavefront using ``parallel_for_each``,
starting with the block at the origin since it has no predecessors.
::
const int N = 64;
std::atomic Count[MAX_LEN/N+1][MAX_LEN/N+1];
void ParallelLCS( const char* x, size_t xlen, const char* y, size_t ylen ) {
// Initialize predecessor counts for blocks.
size_t m = (xlen+N-1)/N;
size_t n = (ylen+N-1)/N;
for( int i=0; i0)+(j>0);
// Roll the wavefront from the origin.
typedef pair block;
block origin(0,0);
oneapi::tbb::parallel_for_each( &origin, &origin+1,
[=]( const block& b, oneapi::tbb::feeder&feeder ) {
// Extract bounds on block
size_t bi = b.first;
size_t bj = b.second;
size_t xl = N*bi+1;
size_t xu = min(xl+N,xlen+1);
size_t yl = N*bj+1;
size_t yu = min(yl+N,ylen+1);
// Process the block
for( size_t i=xl; i