Parallel Algorithms

Parallel Algorithms notes from CMSC751 with Uzi Vishkin.
This class is based on his book Thinking in Parallel Some Basic Data-Parallel Algorithms and Techniques

XMT Language

XMTC is a single-program multiple-data (SPMD) extension of C.

Spawn creates threads
Threads expire at Join
$ represents the number of the thread
PS Ri Rj is an atomic prefix sum
- Stores Ri + Rj in Ri
- Stores the original value of Ri in Rj

int x = 0;

// Spawn n threads
spawn(0, n-1) {
  int e = 1;
  if (A[$] != 0) {
    // Sets e=x and increments x
    ps(e,x);
  }
}

Models

PRAM

Parallel Random-Access Machine/Model
You're given n synchronous processors each with local memory and access to a shared memory.
Each processor can write to shared memory, read to shared memory, or do computation in local memory.
You tell each processor what to do at each time step.

Types of PRAM

exclusive-read exclusive-write (EREW)
concurrent-read exclusive-write (CREW)
concurrent-read concurrent-write (CRCW)
- Arbitrary CRCW - an arbitrary processor writing succeeds
- Priority CRCW - the lowest numbered processor writing succeeds
- Common CRCW - writing succeeds only if all processors write the same value

Drawbacks

Does not reveal how the algorithm will run on PRAMs with different number of proc
Fully specifying allocation requires an unnecessary level of detail

Work Depth

You provide a sequence of instructions. At each time step, you specify the number of parallel operations.

WD-presentation Sufficiency Theorem

Given an algorithm in WD mode that takes $\displaystyle x=x(n)$ operations and $\displaystyle d=d(n)$ time, the algorithm can be implemented in any p-processor PRAM with $\displaystyle O(x/p + d)$ time.

Notes

Other resources call this Brent's theorem
$\displaystyle x$ is the work and $\displaystyle d$ is the depth

Speedup

A parallel algorithm is work-optimal if W(n) grows asymptotically the same as T(n).
A work-optimal parallel algorithm is work-time-optimal if T(n) cannot be improved by another work-optimal algorithm.
If T(n) is best known and W(n) matches it, this is called linear-speedup

NC Theory

Nick's Class Theory

Good serial algorithms: Poly time
Good parallel algorithm: poly-log $\displaystyle O(\log ^c n)$ time, poly processors

Technique: Balanced Binary Trees

Example Applications:

Prefix sum
Largest element
Nearest-one element
Compaction

Inputs

Array A[1..n] of elements
Associative binary operation, denoted * (e.g. addition, multiplication, min, max)

Outputs

Array B containing B(i) = A(1) * ... * A(i)

Algorithm

$\displaystyle O(n)$ work and $\displaystyle O(\log n)$ time

for i, 1 <= i <= n pardo
  B(0, i) = A(i)
  // Summation step up the tree
  for h=1 to log n
    B(h, i) = B(h-1, 2i-1) * B(h-1, 2i)
  // Down the tree
  for h=log n to 1
    if i even, i <= n/2^h
      C(h, i) = C(h+1, i/2)
    if i == 1
      C(h, i) = B(h, i)
    if i odd, 3 <= i <= n/2^h
      C(h, i) = C(h+1, i/2) * B(h, i)
return C(0, :)

Technique: Merge Sorting Cluster

Technique: Partitioning

Merging: Given two sorted arrays, A and B, create a sorted array C consisting of elements in A and B
Ranking: Given an element x, find Rank(x, A) = i such that $\displaystyle A(i) \leq x \leq A(i+1)$

Equivalence of Merging and Ranking

Given an algorithm for merging, we know A(i) belongs in C(j) so Rank(A(i), B) = j-i
Given an algorithm for ranking, we get A(i) belongs in C(j) where j = Rank(A(i), B) + i

Naive algorithm

Apply binary search element wise on concurrent read system. O(log n) time, O(nlog n) work

for 1 <= i <= n pardo
  Rank(A(i), B)
  Rank(B(i), A)

Partitioning paradigm

Input size: n

Partition the input into p jobs of size approx. n/p
Do small jobs concurrently using a separate (possibly serial) algo for each.

Example: Total work O(n), total time O(log n)

// Partitioning
for 1 <= i <= p pardo
  b(i) = Rank((n/p)(i-1)+1, B)
  a(i) = Rank((n/p)(i-1)+1, A)
// Total slice size is <= 2n/p. Total 2p slices.
// E.g. slice size is 2 * log(n) if p=n/log(n).
// Work
for 1 <= i <= p pardo
  k = ceil(b(i)/(n/p)) * (n/p)
  merge slice A[(n/p)(i-1)+1:min(a(i), (n/p)i+1)] and B[b(i):min(b(i+1), k)]

Techinque: Divide and Conquer

Merge Sort

Input: Array A[1..n]
Complexity:
Time: $\displaystyle T(n) \leq T(n/2) + \alpha \log n$
Work: $\displaystyle T(n) \leq 2 * T(n/2) + \beta n$
Time $\displaystyle O(\log^2 n)$. Work: $\displaystyle O(n \log n)$

MergeSort(A, B):
  if n == 1
    return B(1) = A(1)
  call in parallel:
    MergeSort(A[1:n/2], C[1:n/2])
    MergeSort(A[n/2+1:n], C[n/2+1:n])
  Merge C[1:n/2] and C[n/2:n] using O(log n) algorithm

Technique: Informal Work-Depth (IWD) and Accelerating Cascades

Technique: Accelerating Cascades

Consider: for problem of size n, there are two parallel algorithms.

Algo A: $\displaystyle W_1(n)$ work, $\displaystyle T_1(n)$ time
Algo B: Work $\displaystyle W_2(n) \gt W_1(n)$. Time $\displaystyle T_2(n) \lt T_1(n)$. Faster but less efficient.

Assume Algo A is a "reducing algorithm"
Then we start with Algo A until the size is below some threshold. Then switch to Algo B.

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