mainpage.dox

/**
\mainpage The mainpage documentation
 * FlashGraph is an SSD-based graph analysis framework that we designed to
 * process graphs with billions of vertices and hundreds of billions of edges
 * or even larger. We extend FlashGraph to support processing more data
 * structures such as sparse matrices and dense matrices. As such, FlashGraph
 * is now able to support a wide variety of data mining and machine learning
 * algorithms. We address the entire data analysis framework with FlashGraph-ng.
 *
 * The current implementation of FlashGraph-ng has four main components:
 * - _SAFS_: a user-space filesystem designed for large SSD arrays.
 *   It is capable of achieving the maximal I/O throughput from SSD arrays
 *   (millions of I/O per second for random I/O and tens of gigabytes per second
 *   for sequential I/O). The I/O performance is further magnified by
 *   the light-weight page cache if the workload can generate cache hits.
 *
 * - _FlashGraph_: a general-purpose graph analysis framework that exposes
 *   vertex-centric programming interface for users to express varieties of
 *   graph algorithms. FlashGraph scales graph computation to large graphs
 *   by keeping the edges of a graph on SSDs and computation state in memory.
 *   With smart I/O scheduling, FlashGraph is able to achieve performance
 *   comparable to state-of-art in-memory graph analysis frameworks and
 *   significantly outperforms state-of-art distributed graph analysis
 *   frameworks while being able to scale to graphs with billions of vertices
 *   and hundreds of billions of edges.
 *
 * - _FlashMatrix_: a matrix computation engine that provides a small set of
 *   generalized matrix operations on sparse matrices and dense matrices to
 *   express varieties of data mining and machine learning algorithms.
 *   For certain graph algorithms such as PageRank, which can be formulated as
 *   sparse matrix multiplication, FlashMatrix is able to significantly
 *   outperform FlashGraph.
 *
 * - _FlashR_: extends the existing R programming framework to process datasets
 *   at a scale of terabytes. FlashR integrates the matrices and generalized
 *   operators of FlashMatrix with R so that R users can implement many data
 *   mining and machine learning algorithms completely in R with performance
 *   comparable to optimized C implementations. FlashR reimplements
 *   many existing R functions for matrices to provide users a familiar
 *   R programming environment. FlashR is implemented as a regular R package.
 *
 * The figure below shows the architecture of FlashGraph-ng. At the bottem is
 * SAFS, which sits on top of an array of SSDs and exposes a unified asynchronous
 * I/O interface to the data analysis frameworks. On the left is FlashGraph,
 * which exposes a vertex-centric programming interface for users to express
 * a varieties of graph algorithms. FlashGraph contains a set of graph algorithm
 * library written in C++. The graph library is integrated with R so that
 * R users can invoke the graph algorithms in R directly. On the right,
 * FlashMatrix provides both in-memory and external-memory vector and
 * matrix implementations as well as a small set of generalized operators
 * to perform computation on the vectors and matrices. FlashMatrix has
 * an optimizer that optimizes a sequence of operations to achieve performance
 * of an application comparable to a manually optimized C/C++ implementation.
 * FlashR integrates the generalied operators in FlashMatrix to R and
 * reimplements the existing R matrix operations with FlashMatrix.
 * In the future, we will provide an R compiler so that R users can implement
 * the user-defined functions in R and pass them to the generalized operators
 * to perform actual computation.
 * ![Architecture](architecture.png "The architecture of FlashGraph-ng")
 */