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• DISCRETE-EVENT METHOD COMPUTATIONS ORGANIZING FOR PROCESSING LARGE SPARSE UNSTRUCTURED MATRIXES ON RCS

  А.v. Podoprigora

  189-197

  2021-10-05

  Abstract ▼

  Increasing models complexity objects and processes study, in different sphere of science and technology, set up plenty issues to necessary to use high-performance computing systems. Arrays matrix processing by cluster multiprocessor computing systems in conjunction special methods aimed at organizing parallel computations, basically obtain computing performance system is quite high. However, that computational efficiency is not observed for all types of matrices. Matrix structure be in a position contain large amount of insignificant elements, large dimension and unstructured portrait. Calculation execute for described kind of matrices on cluster multiprocessor computing system couldn't achieve close peak performance. Considering that processing methods leave out the complex structure of the matrix being processed. As a result, the performance of the system is significantly reduced. The development of cluster MCS methods doesn't allow for full ensure high performance for class of problems processing of large sparse unstructured matrices. Rigid architecture of processor commutation net doesn’t take into account the peculiarities of such matrices, and lead to non-uniformity loading processor. To achieve performance close the peak for tasks large sparse unstructured matrices processing necessary to use reconfigurable compu-ting systems. RCS architecture allows adapting computation structure to the problem solved. This makes it possible to organize pipeline processing, such a way that computational resource RCS used only for informational significant operations. In addition using generally accepted methods for structural organization of high-performance computing for RCS, it is necessary to develop a format for storing and transferring large sparse unstructured matrices, to determine the principles of constructing basic matrix macro-operations and the possibility of organizing composite dis-crete-event matrix functions for solving applied problems. Сconsequently method founding laid allows organizing computations operands, which are large sparse unstructured matrices. The application this method for organizing computations can significantly increase productivity, and provide an increase in the efficiency of such a system

• IMPROVING REAL PERFORMANCE OF RCS WHEN SOLVING DIGITAL IMAGE PROCESSING TASKS USING FAST FOURIER TRANSFORM

  A.V. Chkan

  2021-02-25

  Abstract ▼

  The article discusses the issues of digital processing of images of large dimensions in real
  time using reconfigurable computer systems (RCS) on the base of programmable logic arrays
  (FPGAs). RCS belongs to the class of high-performance multiprocessor computing systems that
  have a programmable architecture that allows configuring the structure of a computer system and
  optimally adjusting it to the algorithms of the solved task. At the same time, optimization of the
  computational structure of the task reduced to the development and implementation of parallel
  algorithms corresponding to the specifics of the RCS architecture used. All this allows to effectively
  using RCS to solve a wide class of digital signal processing tasks. Offered are methods of increasing
  specific and real performance of RCS when solving digital image processing problems
  using fast Fourier transform (FFT). Using the example of a procedure for filtering images in the
  frequency domain, the main computational steps and methods for optimizing them based on the
  properties of the FFT algorithm are discussed. The use of optimization allows to significantly reducing
  both the amount of computation and the amount of hardware resources of the FPGA andincrease the performance of RCS for image processing tasks. The FPGA resources freed because
  of the optimization of the computational structure can be uses to further parallelize calculations
  and accelerate the processing of incoming data. The advantages of presenting data in fixed-point
  format when performing calculations on RCS are showed. The use of a fixed point allows not only
  to increase the specific and real performance of a computer system compared to a floating point
  due to the properties of the format, but also to use arbitrary data bit capacity, which is relevant for
  most digital signal processing tasks. The solution to the problem of overflow of the bit grid when
  using the fixed-point format using data bit scaling is discussed.

• CONVERTING SOME TYPES OF SEQUENTIAL INFORMATION GRAPHS INTO PARALLEL-PIPELINE FORM

  D.V. Mikhailov

  2021-02-25

  Abstract ▼

  Many digital signal processing tasks can be represented in the form of information
  graphs. Reconfigurable computing systems based on FPGAs can have a structure that directly
  corresponds to the information graph of the problem being solved. The construction of the task
  graph and the subsequent creation of the computational structure can take a significant amount
  of time when performed manually. In this regard, it becomes necessary to create algorithms for
  transforming information graphs that can be performed automatically. The article proposes
  algorithms for transforming homogeneous graphs containing associative operations and mixed
  graphs containing two types of operations, one of which is distributive with respect to the other.
  Transformations of graphs of the first type (consisting of operations of the same type) are reduced
  to the transition from a sequential form of a graph to a pyramidal form to speed up the
  execution of all graph operations. If the available amount of equipment is not enough to impl ement
  all operations of the graph, a transformation is applied that splits the original graph into
  isomorphic subgraphs. The size of the subgraph depends on the available computing resources.
  In this case, the computational structure will correspond to such a subgraph. Transformations
  of graphs of the second type (consisting of operations of two types, some of which are distributive
  with respect to others) are reduced to dividing the graph into subgraphs containing operations
  of the same type, connected in a special way. After that, these subgraphs can be converted
  into a pyramid shape to speed up the execution of all graph operations. In this case, the number
  of vertices with distributive operations can increase significantly, and therefore it may be necessary
  to reduce their number. It follows that when transforming graphs of the second type, it is
  necessary to choose a specific form to which the graph will be reduced, based on the ratio of its
  size and the available computing resource. Thus, the proposed algorithms for transforming
  information graphs of various types can be effectively used in the development of computational
  structures based on FPGAs

• MODIFICATION OF THE IMPLEMENTATION OF THE JACOBI METHOD IN SIMULATING SUPERDIFFUSION OF RADON ON RECONFIGURABLE COMPUTER SYSTEMS

  М.D. Chekina

  198-206

  2021-10-05

  Abstract ▼

  When studying natural objects, the problem of modeling complex systems with a structure that cannot be described by means of Euclidean geometry tools often arises, therefore, fractal geometry and the corresponding mathematical apparatus are used to represent them. So the model of radon transport in an inhomogeneous medium, using superdiffusion, displays real data more accurately than the classical one. An increase in the concentration of radon in the air is one of the signs of an approaching earthquake, which makes it necessary to simulate the propagation of this radioactive inert gas in real time. Reconfigurable computing systems have great potential for solv-ing problems in real time, but the currently existing means for solving systems of linear equations have low efficiency due to the irregular structure of matrices obtained by discretizing the radon superdiffusion model using adaptive grids. The basic subgraph of the Jacobi method is trans-formed as follows: the input data is vectorized, the structure of the frame in which the value of one unknown is calculated is divided into several microframes, parallelizing the calculations in the first microframe, where the sum of the products of the matrix coefficients and the values of the unknowns from the previous iteration is performed. The results obtained are buffered for subse-quent delivery to the second microframe, where the final processing and output of the iteration result takes place. The described approach allows to reduce equipment downtime when solving a system of linear equations with sparse irregular matrices, and gives a speed gain by 5–15 times in comparison with existing methods for solving linear system on reconfigurable computing systems

• TRANSFORMATION METHODS OF COMPUTING STRUCTURE WITH FEEDBACKS FOR EFFECTIVE IMPLEMENTATION ON RECONFIGURABLE COMPUTING SYSTEMS

  S.A. Dudko, I.I. Levin

  2021-12-24

  Abstract ▼

  At present, various computer-aided (CAD) systems are used for solving tasks on reconfigurable
  computing systems (RCS). In most cases, they consist of two main parts: a compiler (translator),
  which translates the source code of a program into a graph-like information and computing
  structure, and a synthesizer, which maps it on an FPGA architecture. As a rule, existing synthesizers process computing structures without any complex optimization. Therefore, the solution, generated
  by the synthesizer, may contain inefficient fragments, which decrease a task solution speed.
  The most common examples of inefficient computing structures are fragments which implement
  recursive expressions. The paper proposes transformation methods for recursive expressions
  (fragments with feedbacks), which allow automatically reduce the data supply interval when solving
  tasks on reconfigurable computing systems. The methods are based on information-equivalent
  transformations of the computing structure of the original task. For each transformation defined a
  set of rules that must be satisfied by the vertices of the computing structure. Applying rules allows
  to perform equivalent transformations not only on simple data structures such as numbers, but
  also on more complex structures (matrices, vectors, tensors, etc.). On the base of the simulation
  results, the developed transformation methods of computing structures with feedbacks allow to
  reduce the task solving time about 2–5 times by reducing the data supply interval. The proposed
  methods are implemented in a prototype of optimizing synthesizer.