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PEM Model

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In computer science, a parallel external memory (PEM) model is a cache-aware, external-memory abstract machine. It is the parallel-computing analogy to the single-processor external memory (EM) model. In a similar way, it is the cache-aware analogy to the parallel random-access machine (PRAM). The PEM model consists of a number of processors, together with their respective private caches and a shared main memory.

Model

Definition

The PEM model is a combination of the EM model and the PRAM model. The PEM model is a computation model which consists of $P$ processors and a two-level memory hierarchy. This memory hierarchy consists of a large external memory (main memory) of size $N$ and $P$ small internal memories (caches). The processors share the main memory. Each cache is exclusive to a single processor. A processor can't access another’s cache. The caches have a size $M$ which is partitioned in blocks of size $B$. The processors can only perform operations on data which are in their cache. The data can be transferred between the main memory and the cache in blocks of size $B$.

I/O complexity

The complexity measure of the PEM model is the I/O complexity, which determines the number of parallel blocks transfers between the main memory and the cache. During a parallel block transfer each processor can transfer a block. So if $P$ processors load parallelly a data block of size $B$ form the main memory into their caches, it is considered as an I/O complexity of $O(1)$ not $O(P)$. A program in the PEM model should minimize the data transfer between main memory and caches and operate as much as possible on the data in the caches.

Read/write conflicts

In the PEM model, there is no direct communication network between the P processors. The processors have to communicate indirectly over the main memory. If multiple processors try to access the same block in main memory concurrently read/write conflicts occur. Like in the PRAM model, three different variations of this problem are considered:

• Concurrent Read Concurrent Write (CRCW): The same block in main memory can be read and written by multiple processors concurrently.
• Concurrent Read Exclusive Write (CREW): The same block in main memory can be read by multiple processors concurrently. Only one processor can write to a block at a time.
• Exclusive Read Exclusive Write (EREW): The same block in main memory cannot be read or written by multiple processors concurrently. Only one processor can access a block at a time.

The following two algorithms solve the CREW and EREW problem if $P\leq B$ processors write to the same block simultaneously. A first approach is to serialize the write operations. Only one processor after the other writes to the block. This results in a total of $P$ parallel block transfers. A second approach needs $O(\log(P))$ parallel block transfers and an additional block for each processor. The main idea is to schedule the write operations in a binary tree fashion and gradually combine the data into a single block. In the first round $P$ processors combine their blocks into $P/2$ blocks. Then $P/2$ processors combine the $P/2$ blocks into $P/4$. This procedure is continued until all the data is combined in one block.

Comparison to other models

Model	Multi-core	Cache-aware
Random-access machine (RAM)	No	No
Parallel random-access machine (PRAM)	Yes	No
External memory (EM)	No	Yes
Parallel external memory (PEM)	Yes	Yes

Examples

Multiway partitioning

Let $M=\{m_{1},...,m_{d-1}\}$ be a vector of d-1 pivots sorted in increasing order. Let A be an unordered set of N elements. A d-way partition of A is a set $\Pi =\{A_{1},...,A_{d}\}$ , where $\cup _{i=1}^{d}A_{i}=A$ and $A_{i}\cap A_{j}=\emptyset$ for $1\leq i<j\leq d$. $A_{i}$ is called the i-th bucket. The number of elements in $A_{i}$ is greater than $m_{i-1}$ and smaller than $m_{i}^{2}$. In the following algorithm the input is partitioned into N/P-sized contiguous segments $S_{1},...,S_{P}$ in main memory. The processor i primarily works on the segment $S_{i}$. The multiway partitioning algorithm (PEM_DIST_SORT) uses a PEM prefix sum algorithm to calculate the prefix sum with the optimal $O\left({\frac {N}{PB}}+\log P\right)$ I/O complexity. This algorithm simulates an optimal PRAM prefix sum algorithm.

// Compute parallelly a d-way partition on the data segments $S_{i}$
for each processor i in parallel do
    Read the vector of pivots M into the cache.
    Partition $S_{i}$ into d buckets and let vector $M_{i}=\{j_{1}^{i},...,j_{d}^{i}\}$ be the number of items in each bucket.
end for

Run PEM prefix sum on the set of vectors $\{M_{1},...,M_{P}\}$ simultaneously.

// Use the prefix sum vector to compute the final partition
for each processor i in parallel do
    Write elements $S_{i}$ into memory locations offset appropriately by $M_{i-1}$ and $M_{i}$.
end for

Using the prefix sums stored in $M_{P}$ the last processor P calculates the vector B of bucket sizes and returns it.

If the vector of $d=O\left({\frac {M}{B}}\right)$ pivots M and the input set A are located in contiguous memory, then the d-way partitioning problem can be solved in the PEM model with $O\left({\frac {N}{PB}}+\left\lceil {\frac {d}{B}}\right\rceil >\log(P)+d\log(B)\right)$ I/O complexity. The content of the final buckets have to be located in contiguous memory.

Selection

The selection problem is about finding the k-th smallest item in an unordered list A of size N. The following code makes use of PRAMSORT which is a PRAM optimal sorting algorithm which runs in $O(\log N)$, and SELECT, which is a cache optimal single-processor selection algorithm.

if $N\leq P$ then 
    ${\texttt {PRAMSORT}}(A,P)$
    return $A[k]$
end if 

//Find median of each $S_{i}$
for each processor i in parallel do 
    $m_{i}={\texttt {SELECT}}(S_{i},{\frac {N}{2P}})$
end for 

// Sort medians
${\texttt {PRAMSORT}}(\lbrace m_{1},\dots ,m_{2}\rbrace ,P)$

// Partition around median of medians
$t={\texttt {PEMPARTITION}}(A,m_{P/2},P)$

if $k\leq t$ then 
    return ${\texttt {PEMSELECT}}(A[1:t],P,k)$
else 
    return ${\texttt {PEMSELECT}}(A[t+1:N],P,k-t)$
end if

Under the assumption that the input is stored in contiguous memory, PEMSELECT has an I/O complexity of:

$O\left({\frac {N}{PB}}+\log(PB)\cdot \log({\frac {N}{P}})\right)$

Distribution sort

Distribution sort partitions an input list A of size N into d disjoint buckets of similar size. Every bucket is then sorted recursively and the results are combined into a fully sorted list.

If $P=1$ the task is delegated to a cache-optimal single-processor sorting algorithm.

Otherwise the following algorithm is used:

// Sample ${\tfrac {4N}{\sqrt {d}}}$ elements from A
for each processor i in parallel do
    if $M<|S_{i}|$ then
        $d=M/B$
        Load $S_{i}$ in M-sized pages and sort pages individually
    else
        $d=|S_{i}|$
        Load and sort $S_{i}$ as single page
    end if
    Pick every ${\sqrt {d}}/4$'th element from each sorted memory page into contiguous vector $R^{i}$ of samples
end for 

in parallel do
    Combine vectors $R^{1}\dots R^{P}$ into a single contiguous vector ${\mathcal {R}}$
    Make ${\sqrt {d}}$ copies of ${\mathcal {R}}$: ${\mathcal {R}}_{1}\dots {\mathcal {R}}_{\sqrt {d}}$
end do

// Find ${\sqrt {d}}$ pivots ${\mathcal {M}}[j]$
for $j=1$ to ${\sqrt {d}}$ in parallel do
    ${\mathcal {M}}[j]={\texttt {PEMSELECT}}({\mathcal {R}}_{i},{\tfrac {P}{\sqrt {d}}},{\tfrac {j\cdot 4N}{d}})$
end for

Pack pivots in contiguous array ${\mathcal {M}}$

// Partition Aaround pivots into buckets ${\mathcal {B}}$
${\mathcal {B}}={\texttt {PEMMULTIPARTITION}}(A[1:N],{\mathcal {M}},{\sqrt {d}},P)$

// Recursively sort buckets
for $j=1$ to ${\sqrt {d}}+1$ in parallel do
    recursively call ${\texttt {PEMDISTSORT}}$ on bucket jof size ${\mathcal {B}}[j]$
    using $O\left(\left\lceil {\tfrac {{\mathcal {B}}[j]}{N/P}}\right\rceil \right)$ processors responsible for elements in bucket j
end for

The I/O complexity of PEMDISTSORT is:

$O\left(\left\lceil {\frac {N}{PB}}\right\rceil \left(\log _{d}P+\log _{M/B}{\frac {N}{PB}}\right)+f(N,P,d)\cdot \log _{d}P\right)$

where

$f(N,P,d)=O\left(\log {\frac {PB}{\sqrt {d}}}\log {\frac {N}{P}}+\left\lceil {\frac {\sqrt {d}}{B}}\log P+{\sqrt {d}}\log B\right\rceil \right)$

If the number of processors is chosen that $f(N,P,d)=O\left(\left\lceil {\tfrac {N}{PB}}\right\rceil \right)$and $M<B^{O(1)}$ the I/O complexity is then:

$O\left({\frac {N}{PB}}\log _{M/B}{\frac {N}{B}}\right)$

Other PEM algorithms

PEM Algorithm	I/O complexity	Constraints
Mergesort	$O\left({\frac {N}{PB}}\log _{\frac {M}{B}}{\frac {N}{B}}\right)={\textrm {sort}}_{P}(N)$	$P\leq {\frac {N}{B^{2}}},M=B^{O(1)}$
List ranking	$O\left({\textrm {sort}}_{P}(N)\right)$	$P\leq {\frac {N/B^{2}}{\log B\cdot \log ^{O(1)}N}},M=B^{O(1)}$
Euler tour	$O\left({\textrm {sort}}_{P}(N)\right)$	$P\leq {\frac {N}{B^{2}}},M=B^{O(1)}$
Expression tree evaluation	$O\left({\textrm {sort}}_{P}(N)\right)$	$P\leq {\frac {N}{B^{2}\log B\cdot \log ^{O(1)}N}},M=B^{O(1)}$
Finding a MST	$O\left({\textrm {sort}}_{P}(|V|)+{\textrm {sort}}_{P}(|E|)\log {\tfrac {|V|}{pB}}\right)$	$p\leq {\frac {|V|+|E|}{B^{2}\log B\cdot \log ^{O(1)}N}},M=B^{O(1)}$

Where ${\textrm {sort}}_{P}(N)$ is the time it takes to sort N items with P processors in the PEM model.

See also

• Parallel random-access machine (PRAM)
• Random-access machine (RAM)
• External memory (EM)