|  | | Introduction |  | 
			 
				Welcome to the Colt Project. Colt provides a set of Open Source Libraries for
				High Performance Scientific and Technical Computing in Java.
				 
				Scientific and technical computing, 
				as, for example, carried out at CERN, is characterized by demanding problem sizes 
				and a need for high performance at reasonably small memory footprint. There is 
				a perception by many that the Java language is unsuited for such work. However, 
				recent trends in its evolution suggest that it may soon be a major player in performance 
				sensitive scientific and technical computing. For example, IBM Watson's Ninja 
				project showed that Java can indeed perform BLAS matrix computations up to 
				90% as fast as optimized Fortran. The Java 
				Grande Forum Numerics Working Group provides a focal point for information 
				on numerical computing in Java. With the performance gap steadily closing, Java 
				has recently found increased adoption in the field. The reasons include ease of 
				use, cross-platform nature, built-in support for multi-threading, network friendly 
				APIs and a healthy pool of available developers. Still, these efforts are to a 
				significant degree hindered by the lack of foundation toolkits broadly available 
				and conveniently accessible in C and Fortran.
				  The latest stable Colt release breaks the  1.9 Gflop/s barrier
			      on JDK ibm-1.4.1, RedHat 9.0, 2x IntelXeon@2.8 GHz.
      			 This distribution provides an infrastructure for scalable scientific 
  and technical computing in Java. It is particularly useful in the domain of 
  High Energy Physics at CERN: It contains, among others, efficient and usable 
  data structures and algorithms for Off-line and On-line Data Analysis, Linear 
  Algebra, Multi-dimensional arrays, Statistics, Histogramming, Monte Carlo Simulation, 
  Parallel & Concurrent Programming. It summons some of the best concepts, 
  designs and implementations thought up over time by the community, ports or 
  improves them and introduces new approaches where need arises. In overlapping 
  areas, it is competitive or superior to toolkits such as STL , 
  Root , HTL , CLHEP , TNT , 
  GSL , 
  C-RAND 
  / WIN-RAND , (all C/C++) as well as IBM Array , JDK 1.2 Collections framework  (all Java), in terms of performance (!), functionality and (re)usability.
			 This distribution consists of several free Java libraries, 
  for user convenience bundled under one single uniform umbrella. Namely the Colt 
  library, the Jet library, the CoreJava library, and the Concurrent library. 
  The Colt library provides fundamental general-purpose data 
  structures optimized for numerical data, such as resizable arrays, dense and 
  sparse matrices (multi-dimensional arrays), linear algebra, associative containers 
  and buffer management. The Jet library contains mathematical and statistical 
  tools for data analysis, powerful histogramming functionality, Random Number 
  Generators and Distributions useful for (event) simulations, and more. The CoreJava 
  library contains C-like print formatting. The Concurrent library contains standardized, 
  efficient utility classes commonly encountered in parallel & concurrent 
  programming.
			 A distribution download includes HTML API documentation 
  and source codes for all libraries, as well as one single cross-platform shared 
  library file, colt.jar, containing the distribution compiled 
  to immediately executable format. Thus, a user can start to work by setting 
  one single environment variable. He/she never needs to bother about compilation/architecture/linker 
  issues.
			 | 
 | Features |  | 
		
		
 	   
        | Templated Lists and Maps | Dynamically resizing lists 
		      holding objects or primitive data types such as int,double, 
		      etc. Operations on primitive arrays, algorithms on Colt lists and JAL algorithms 
		      (see below) can freely be mixed at zero copy overhead. More 
		      details. Automatically growing and shrinking maps holding objects or 
		      primitive data types such as int, double, etc. More 
		      details. Space efficient high performance BitVectors and BitMatrices. 
 		     More details |  
        | Templated Multi-dimensional matrices | Dense and sparse fixed sized (non-resizable) 1,2, 3 and d-dimensional matrices 
		      holding objects or primitive data types such as int, double, 
		      etc; Also known as multi-dimensional arrays or Data Cubes. 
		      More details. |  
        | Linear Algebra | Standard matrix operations and decompositions. LU, QR, Cholesky, Eigenvalue, Singular value. 
		      More details. |  
        | Histogramming | Compact, extensible, modular 
		      and performant histogramming functionality. AIDA offers the histogramming 
		      features of HTL and HBOOK. More details here 
		      and also there. |  
        | Mathematics | Tools for basic and advanced 
      mathematics: Arithmetics and Algebra, Polynomials and Chebyshev series, 
      Bessel and Airy functions, Constants and Units, Trigonometric functions, 
      etc. More details. |  
        | Statistics | Tools for basic and advanced 
      statistics: Estimators, Gamma functions, Beta functions, Probabilities, 
      Special integrals, etc. More 
      details. |  
        | Random Numbers and Random Sampling | Strong 
      yet quick. Partly a port of CLHEP. More details here 
      and there and 
      also there. |  
        | util.concurrent | Efficient utility classes commonly encountered in parallel & concurrent programming. 
      More 
      details. |  | 
 | Design Goals |  | 
		
		
 	   
        | Efficiency | Routines are typically fast both 
      due to the chosen algorithms and datastructures as well as due to careful 
      implementation. For comparative benchmarks the latest stable JDK is recommended. |  
        | User friendliness | To the casual user this 
      is a high level object oriented toolkit, consisting of classes which directly 
      provide most frequently needed functionality. Most users will never need 
      to extend or modify any code. Classes are cleanly separated into several 
      mostly self contained packages. |  
        | Expert friendliness | In our view, implementations 
      should not be hidden. Instead, a user, according to his or her likings, 
      should be encouraged to look under the hood and even tinker with the code. 
      Not only the public API is extensively documented, but also internal code. 
      Users who wish to enrich, modify or customize functionality should be able 
      to do so without much effort. |  
        | Safety | Most methods defensively check preconditions 
      and throw appropriate exceptions. However, almost none of them are synchronized. |  | 
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