Dátové štruktúry pre externú pamäť

External memory model, I/O model

Introduction

• big and slow disk, fast memory of a limited size M words
• disk reads/writes in blocks of size B words
• when analyzing algorithms, count how many disk reads/writes (memory transfers) are done

Example: scanning through n elements (e.g. to compute their sum): Theta(ceiling of n/B) memory transfers

B-trees

• I/O cost of binary search tree?
• parameter T related to block size B (so that each node fits into a constant number of blocks)
• each node v has some number v.n of keys
  • if it is an internal node, it has v.n+1 children
  • keys in a node are sorted
  • each subtree contains only values between two successive keys in the parent
• all leaves are in the same depth
• each node except root has at least T-1 keys and each internal node except root has at least T children
• each node has at most 2T-1 keys and at most 2T children
• height is O(log_T n)

Search:

• O(log_T n) block reads

Insert:

• find a leaf where the new key belongs
• if leaf is full (2T-1 keys), split into two equal parts of size T-1 and insert median to the parent (recursively)
  • new element is inserted to one of the new leaves
  • we continue splitting ancestors until we find a node which is not full or until we reach the root. If root is full, we create a new root with 2 children (increasing the height of the tree)
• O(log_T n) transfers

Sorting

mergesort

• I/O cost of ordinary implementation?
• create sorted runs of size M using O(N/B) transfers
• repeatedly merge M/B-1 runs into one in 1 pass, this uses N/B disk reads and writes
• log_{M/B-1} (N/M) passes

Summary

• B-trees can do search tree operations (insert, delete, search/predecessor) in Theta(log_{B+1} n) = Theta(log n / log (B+1)) memeory transfers
  • number of memory transfers compared to usual search tree running time: factor of 1/log B
• search optimal in comparison model
  • proof outline through information theory
  • goal of the seach is to tell us between which two elements is query located
  • there are Theta(n) possible answers, thus Theta(log n) bits of information
  • in normal comparison model one comparison of query to an elements of the set gives us at most 1 bit of information
  • in I/O model reading a block of B elements and comparing query to them gives us in the beste case its position within these B ele,ents, i.e. Theta(log B) bits
  • to get Theta(log n) bits of infomration for the answer, we need Theta(log n/log B) block reads
  • this can be transformed to a more formal and precise proof
• sorting O((N/B) log_{M/B} (N/B)) memory trasfers
  • number of memory transfers compared to usual sorting running time: factor of more than 1/B
  • also much better than sorting using B-trees, which would take O(N * log_B n)

Cache oblivious model

Introduction

• in the I/O model the algorithm explicitly requests block transfers, knows B, controls memory allocation
• in the cache oblivious model algorithm does not know B or M, memory operates as a cache
• M/B slots, each holding one block from disk
• algorithm requests reading a word from disk
  • if the block containing this word in cache, no transfer
  • replace one slot with block holding requested item, write original block if needed (1 or 2 transfers)
  • which one to replace: classical on-line problem of paging

Paging

• cache has size k = M/B slots, each holds one page (above called block)
• sequence of page requests, if the requested page not in memory not in memory, bring it in and remove some other page (page fault)
  • goal is to minimize the number of page faults
• optimal offline algorithm (knows the whole sequence of page requests)
  • At a page fault remove the page that will not be used for the longest time
• example of an on-line algoritm: FIFO:
  • at a page fault remove page which is in the memory longest time
  • uses at most k times as many page faults as the optimal algorithm (k-competitive)
  • no deterministic alg. can be better than k-competitive
  • it is conservative: in a segment of requests containing at most k distinct pages it does at most k page faults
  • every conservative alg. is k-competitive
• Compare a conservative paging alg. (e.g. FIFO) on memory with k blocks to optimum offline alg. on a smaller memory of size h - competitive ratio k/(k-h)
  • if h = k/2, we get competitive ratio 2
  • divide input sequence into maximal blocks, each containing k distinct elements (first element of the next block is distinct from the k elements of the previous block)
  • FIFO uses at most k page faults in each block
  • optimum has at most h sets of a block in memory at the beginning - at least k-h pages will cause a fault
  • in fact we can prove k/(k-h+1), works even for k=h

Back to cache-oblivious model

• we analyze algorithms under the assumptiom that paging algorithm uses off-line optimum
• instead it could use e.g. FIFO in memory 2M and increase the number of transfers by a constant factor
• advantages of cache-oblivious model:
  • may adapt to changing M, B
  • good for a whole memory hierarchy (several levels of cache, disk, network,...)
• scanning still Theta(ceiling of N/B)
• search trees still Theta(log_{B+1} N)
• sorting Theta(n/B log_{M/B}(N/B)) but requires big enough M i.e. M=Omega(B^{1+epsilon})

Static cache-oblivious search trees

• first published by Harald Prokop, Master thesis, MIT 1999 [1]
• alternative to a binary search
  • exercise: how many transfers for binary search?
• use perfectly balanced binary search tree
• search algorithm as usual (follows path from root down as usual)
• nodes are stored on disk in some order which we can choose
  • the goal is to choose it so that for any B, M and any search we use only few blocks
  • for example, what would happen if we store nodes in pre-order or level-order?
  • instead of simple orders, we will use van Emde Boas order (later in the course we will see van Emde Boas trees for integer keys)

van Emde Boas order

• split tree of height lg n into top and bottom, each of height (1/2) lg n
• top part is a small tree with about sqrt(n) vertices
• bottom part consists of about sqrt(n) small trees, each size about sqrt(n)
• each of these small trees is processed recursively and the results are concatenated
• for example a tree with 4 levels is split into 5 trees with 2 levels, resulting in the following ordering:

          1
     2           3
  4     7    10    13
5  6  8  9 11 12 14 15

• if B = (1/2) lg n, we need only 2 trasfers
• but we will show next that for any B<=M/2 and for any path from the root we need only Theta(log_{B+1} n) block transfers

Analysis

• find level of recursion where each smaller tree has height k such that size of these trees is <=B and the next higher level has tree size >B
• (1/2) lg B <= k <= lg B
• length of path lg N, visits lg N / k trees of height k, which is Theta(lg N / lg B)
• each tree of height k occupies at most B cells in memory (assuming each node fits int a word, otherwise everything is multipled by a constant factor...)
• traversing the tree might need up to 2 memory transfers if there is a block boundary inside interval for the tree (whole tree fits in memeory)

Dynamic cache oblivious trees

• only rough sketch

uses "ordered file maintenance"

• maintain n items in an array of size O(n) with gaps of size O(1)
• updates: delete item, insert item between given two items (similar to linked list)
• update rewrites interval of size at most O(log^2 n) amortized in O(1) scans
• done by keeping appropriate density in a hierarchy of intervals
• we will not cover details

simpler version of data structure

• our keep elements in "ordered file" in a sorted order
• build a full binary tree on top of array (segment tree)
• each node stores maximum in its subtree
• tree stored in vEB order
• when array gets too full, double the size, rebuild everything

search

• search checks max in left child and decides to move left or right
• basically follows a path from root, uses O(log_B n) transfers

update

• search to find the leaf
• update "ordered file", resulting in changes in interval of size L (O(log^2 n) amortized)
• then update all ancestors of these values in the tree by postorder traversal

analysis of update

• again let k be height of vEM subtrees which have size <=B but next higher level has size >B
• (1) first consider bottom 2 levels: O(1+L/B) trees of size k need updating (+1 from ceilings)
• each tree stored in <=2 blocks
• postorder traversals revisits parent block between child blocks, but if M>=4B, stays in memory
• overall O(log^2 n /B) transfers for bottom two layers
• (2) part of the tree up to lca of all changed leaves: O(L/B) nodes, even if one trasfer per each, we are fine
• (3) path from lca to root O(log_B n) transfers as in search
• (1)+(2)+(3): O(log^2 n / B + log_B n) - too big for small values of B

improvement of update

• split sorted data into blocks, each block of size between (1/4) lg n and lg n
• each block stored sequentially on disk,
• minimum element from each block used as a leaf in the above data structure, now contains only O(n/log n) elements
• update/search inside block: 1 scan, O(log n/B)
• if a block gets too large, split into two blocks (similarly to B-trees)
• if a block gets too small, connect with adjacent block, maybe resplit
• spliting/connecting propagates as update to the structure for n/log n items
• but this happens only once very log n updates, so amortized time for update divided by log n
• however, O(log_B n) term still necessary for search

Sources

• Prednaska L07 Erika Demaina z MIT: http://courses.csail.mit.edu/6.851/spring12/lectures/
• Kapitola o B-trees z Cormen, Thomas H., Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms. (ja som pouzila 3. vydanie)
• online algoritmy a paging http://courses.csail.mit.edu/6.854/03/scribe/scribe19.ps