Download Programming Pearls, 2Ed

Programming Pearls, 2Ed
(ISBN: 0-201-65788-0)
Column 1 – Cracking the Oyster
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The right problem
Bitmap (BitSet) data structure
Multi-pass algorithms
Time-Space tradeoff and one that isn’t
Simple Design
Stages of program design
Column 2 – Aha! Algorithms
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Sorting
Binary search (find first occurrence of t)
Signatures
Problem definition
A problem’s solver perspective – know the proper time to code
Column 3 – Data Structures Programs
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Rework repeated code into arrays
Encapsulate data structures
Use advanced tools: name-value pairs, spreadsheets, databases, etc
Let the data structure the program
Column 4 – Writing Correct Programs
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Initialization, Preservation, Termination
Assertions (Invariants)
Sequential control structures
Selection control structures
Iteration control structures
Functions: precondition, postcondition
Column 6 – Perspective on Performance
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Problem definition
System structure – decomposition to modules
Algorithms and data structures
o Algorithm tuning
o Data structure reorganization
Code tuning
System software
Hardware
If you need a little speedup – work at the best level
If you need a big speedup – work at many levels
Column 7 – The back of the Envelope
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Two answers are better than one
Quick checks
Rules of thumb: “Rule of 72”
Pi seconds is a nanocentury (1 year = 3.155 x 10^7 seconds)
Practice
Safety factors
Little’s Law:
o The average number of things in the system is the product of the
average rate at which things leave the system and the average
time each one spends in the system.
o The average number of objects in a queue is the product of the
entry rate and the average holding time.
Column 8 – Algorithm Design Techniques
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Save state to avoid recomputation
Preprocess information into data structures
Divide-and-Conquer algorithms
Scanning algorithms: N-1 => N
Cumulatives (cumulative arrays)
Lower bounds
Column 9 – Code Tuning
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Caching
Bulk memory allocations (in C)
Functions, macros and inline code
Sentinel
Loop unrolling
Data structure augmentation
Recursion -> Iteration
Swap -> Inline + Optimize
Integer remainders: ( sth % N ) = ( sth & N ) or ( sth – N if sth in [N, 2N] )
Column 10 – Squeezing Space
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Know the cost of space
“Hot Spots” of space
Don’t store, recompute
Sparse data structures
Data compression – alternative ways to store the same data (hashing)
Allocation policies – dynamic allocation
Variable-length records
Garbage collection
Sharing storage
Cache-sensitive memory layouts
Tradeoffs
Environment
Column 11 – Sorting
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Insertion sort
Quicksort:
o One-sided partition (O(n^2) worst case with N equals!)
o Two-sided partition
o Partition around random element or Pivot
o Don’t partition short arrays and use insertion sort when finished
Column 13 – Searching
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Set:
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Sorted array
Sorted linked list (more memory, worse memory access patterns)
Binary tree (TreeSet)
Hashtable (HashSet)
Bitset (32 bits per word)
Column 14 – Heaps
See “Beginning Algorithms.doc”
Column 15 – String of Pearls
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Hashtable
Balanced trees
Suffix arrays
Longest duplicated substring:
o Build suffix array
o Sort suffix array (to bring together equal siffixes)
Generate random text – Markov chain:
o Build suffix array pointing to word boundaries and sort it
o Hashtable: prefix -> all possible suffixes
Appendix 1 – A Catalog of Algorithms
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Sorting:
o Insertion sort
o Quicksort
o Heapsort
o Mergesort
o Bitmap sort
o Multiple-pass sort
Searching:
o Sequential search
o Binary search
o Hashtable
o Binary search trees
o Key indexing (keys are used as array indexes)
Appendix 4 – Rules for Code Tuning
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Space-For-Time:
o Data structure augmentation
o Store precomputed results
o Caching
 Can backfire if locality is not present in the data
o Lazy Evaluation
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Time-For-Space:
o Packing – dense storage
 Sparse arrays
 Overlaying
o Interpreters
o Function definition
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Loop:
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Code motion out of loop
Combining tests (sentinel!)
Loop unrolling
Loop fusion
Logic:
o Exploit algebraic identities – replace evaluation by equiv expression
o Short-circuiting monotone functions – exit from loop no later than
o Reordering tests
o Precompute logical function
o Boolean variable elimination
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Procedure:
Collapsing function hierarchies
Exploit common cases
Coroutines
Transformations on recursive functions
 Iteration
 Iteration using stack
 Tail recursion elimination
 Don’t solve small sub-problems with recursion
o Parallelism
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Expression:
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Compile-time initialization
Exploit algebraic identities
Common subexpression elimination
Pairing computations
Exploit word parallelism
JBoss Course – Fine Tuning Java EE Applications
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Serialization and externalization (default, XML)
o Use collocation, “pass be reference”
o Use JBoss Serialization
o Minimize cluster state replication
Remote calls (EJB, RMI, etc)
o Combine many to one
o Minimal number of arguments
Locking, transactions
o Keep transactions as short as possible
o Fine-tune transactional tags
o No transactions for read-only components
o Optimistic concurrency
Data access, DB
o Cache results
o Fine-tune schema, SQL, stored procedures
o Use indexes
o Adjust transaction isolation levels
JVM, GC