In computer science, aparallel external memory (PEM) model is acache-aware, external-memoryabstract machine.^[1] It is the parallel-computing analogy to the single-processorexternal memory (EM) model. In a similar way, it is the cache-aware analogy to theparallel random-access machine (PRAM). The PEM model consists of a number of processors, together with their respective private caches and a shared main memory.

The PEM model^[1] is a combination of the EM model and the PRAM model. The PEM model is a computation model which consists of${\displaystyle P}$ processors and a two-levelmemory hierarchy. This memory hierarchy consists of a large external memory (main memory) of size${\displaystyle N}$ and${\displaystyle P}$ 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${\displaystyle M}$ which is partitioned in blocks of size${\displaystyle B}$. 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${\displaystyle B}$.

The complexity measure of the PEM model is the I/O complexity,^[1] 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${\displaystyle P}$ processors load parallelly a data block of size${\displaystyle B}$ form the main memory into their caches, it is considered as an I/O complexity of${\displaystyle O(1)}$ not${\displaystyle O(P)}$. 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.

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^[1] 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^[1] solve the CREW and EREW problem if${\displaystyle P\le B}$ 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${\displaystyle P}$ parallel block transfers. A second approach needs${\displaystyle O(\log (P))}$ 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${\displaystyle P}$ processors combine their blocks into${\displaystyle P/2}$ blocks. Then${\displaystyle P/2}$ processors combine the${\displaystyle P/2}$ blocks into${\displaystyle P/4}$. This procedure is continued until all the data is combined in one block.

Let${\displaystyle M=\{m_1,...,m_{d-1}\}}$ be a vector of d-1 pivots sorted in increasing order. LetA be an unordered set of N elements. A d-way partition^[1] ofA is a set${\displaystyle \mathrm{\Pi }=\{A_1,...,A_d\}}$ , where${\displaystyle {\cup }_{i=1}^d{A}_i=A}$ and${\displaystyle A_i\cap A_j=\mathrm{\varnothing }}$ for.${\displaystyle A_i}$ is called the i-th bucket. The number of elements in${\displaystyle A_i}$ is greater than${\displaystyle m_{i-1}}$ and smaller than${\displaystyle m_i^2}$. In the following algorithm^[1] the input is partitioned into N/P-sized contiguous segments${\displaystyle S_1,...,S_P}$ in main memory. The processor i primarily works on the segment${\displaystyle S_i}$. The multiway partitioning algorithm (PEM_DIST_SORT^[1]) uses a PEMprefix sum algorithm^[1] to calculate the prefix sum with the optimal${\displaystyle O\left(\frac{N}{PB}+\log P\right)}$ I/O complexity. This algorithm simulates an optimal PRAM prefix sum algorithm.

// Compute parallelly a d-way partition on the data segments${\displaystyle S_i}$for each processor iin parallel do    Read the vector of pivotsM into the cache.    Partition${\displaystyle S_i}$ into d buckets and let vector${\displaystyle M_i=\{j_1^i,...,j_d^i\}}$ be the number of items in each bucket.end forRun PEM prefix sum on the set of vectors${\displaystyle \{M_1,...,M_P\}}$ simultaneously.// Use the prefix sum vector to compute the final partitionfor each processor iin parallel do    Write elements${\displaystyle S_i}$ into memory locations offset appropriately by${\displaystyle M_{i-1}}$ and${\displaystyle M_i}$.end forUsing the prefix sums stored in${\displaystyle M_P}$ the last processor P calculates the vectorB of bucket sizes and returns it.

If the vector of${\displaystyle d=O\left(\frac{M}{B}\right)}$ 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${\displaystyle O\left(\frac{N}{PB}+\lceil \frac{d}{B}\rceil >\log (P)+d\log (B)\right)}$ I/O complexity. The content of the final buckets have to be located in contiguous memory.

Theselection problem is about finding the k-th smallest item in an unordered listA of sizeN.The following code^[1] makes use ofPRAMSORT which is a PRAM optimal sorting algorithm which runs in${\displaystyle O(\log N)}$, andSELECT, which is a cache optimal single-processor selection algorithm.

if${\displaystyle N\le P}$then${\displaystyle \mathtt{\text{PRAMSORT}}(A,P)}$return${\displaystyle A[k]}$end if //Find median of each${\displaystyle S_i}$for each processoriin parallel do${\displaystyle m_i=\mathtt{\text{SELECT}}(S_i,\frac{N}{2P})}$end for // Sort medians${\displaystyle \mathtt{\text{PRAMSORT}}(\{m_1,\dots ,m_2\},P)}$// Partition around median of medians${\displaystyle t=\mathtt{\text{PEMPARTITION}}(A,m_{P/2},P)}$if${\displaystyle k\le t}$thenreturn${\displaystyle \mathtt{\text{PEMSELECT}}(A[1:t],P,k)}$elsereturn${\displaystyle \mathtt{\text{PEMSELECT}}(A[t+1:N],P,k-t)}$end if

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

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

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

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

Otherwise the following algorithm^[1] is used:

// Sample${\displaystyle \frac{4N}{\sqrt{d}}}$ elements fromAforeach processoriin parallel doifthen${\displaystyle d=M/B}$        Load${\displaystyle S_i}$ inM-sized pages and sort pages individuallyelse${\displaystyle d=|S_i|}$        Load and sort${\displaystyle S_i}$ as single pageend if    Pick every${\displaystyle \sqrt{d}/4}$'th element from each sorted memory page into contiguous vector${\displaystyle R^i}$ of samplesend forin parallel do    Combine vectors${\displaystyle R^1\dots R^P}$ into a single contiguous vector${\displaystyle \mathcal{R}}$    Make${\displaystyle \sqrt{d}}$ copies of${\displaystyle \mathcal{R}}$:${\displaystyle {\mathcal{R}}_1\dots {\mathcal{R}}_{\sqrt{d}}}$end do// Find${\displaystyle \sqrt{d}}$ pivots${\displaystyle \mathcal{M}[j]}$for${\displaystyle j=1}$ to${\displaystyle \sqrt{d}}$in parallel do${\displaystyle \mathcal{M}[j]=\mathtt{\text{PEMSELECT}}({\mathcal{R}}_i,\frac{P}{\sqrt{d}},\frac{j\cdot 4N}{d})}$end forPack pivots in contiguous array${\displaystyle \mathcal{M}}$// PartitionAaround pivots into buckets${\displaystyle \mathcal{B}}$${\displaystyle \mathcal{B}=\mathtt{\text{PEMMULTIPARTITION}}(A[1:N],\mathcal{M},\sqrt{d},P)}$// Recursively sort bucketsfor${\displaystyle j=1}$ to${\displaystyle \sqrt{d}+1}$in parallel do    recursively call${\displaystyle \mathtt{\text{PEMDISTSORT}}}$ on bucketjof size${\displaystyle \mathcal{B}[j]}$    using${\displaystyle O\left(\lceil \frac{\mathcal{B}[j]}{N/P}\rceil \right)}$ processors responsible for elements in bucketjend for

The I/O complexity ofPEMDISTSORT is:

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

where

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

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

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

PEM Algorithm	I/O complexity	Constraints
Mergesort[1]	${\displaystyle O\left(\frac{N}{PB}{\log }_{\frac{M}{B}}\frac{N}{B}\right)={\text{sort}}_P(N)}$	${\displaystyle P\le \frac{N}{B^2},M=B^{O(1)}}$
List ranking[2]	${\displaystyle O\left({\text{sort}}_P(N)\right)}$	${\displaystyle P\le \frac{N/B^2}{\log B\cdot {\log }^{O(1)}N},M=B^{O(1)}}$
Euler tour[2]	${\displaystyle O\left({\text{sort}}_P(N)\right)}$	${\displaystyle P\le \frac{N}{B^2},M=B^{O(1)}}$
Expression tree evaluation[2]	${\displaystyle O\left({\text{sort}}_P(N)\right)}$	${\displaystyle P\le \frac{N}{B^2\log B\cdot {\log }^{O(1)}N},M=B^{O(1)}}$
Finding aMST[2]	${\displaystyle O\left({\text{sort}}_P(|V|)+{\text{sort}}_P(|E|)\log \frac{|V|}{pB}\right)}$	${\displaystyle p\le \frac{|V|+|E|}{B^2\log B\cdot {\log }^{O(1)}N},M=B^{O(1)}}$

Where${\displaystyle {\text{sort}}_P(N)}$ is the time it takes to sortN items withP processors in the PEM model.

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

• Algorithms
• Models of computation
• Analysis of parallel algorithms
• External memory algorithms
• Cache (computing)