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List Processing in Real Time on a Serial Computer
- SERIAL COMPUTER, COMM. ACM
, 1977
"... A real-time list processing system is one in which the time required by the elementary list operations (e.g. CONS, CAR, COR, RPLACA, RPLACD, EQ, and ATOM in LISP) is bounded by a (small) constant. Classical implementations of list processing systems lack this property because allocating a list cell ..."
Abstract
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Cited by 202 (13 self)
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A real-time list processing system is one in which the time required by the elementary list operations (e.g. CONS, CAR, COR, RPLACA, RPLACD, EQ, and ATOM in LISP) is bounded by a (small) constant. Classical implementations of list processing systems lack this property because allocating a list cell from the heap may cause a garbage collection, which process requires time proportional to the heap size to finish. A real-time list processing system is presented which continuously reclaims garbage, including directed cycles, while linearizing and compacting the accessible cells into contiguous locations to avoid fragmenting the free storage pool. The program is small and requires no time-sharing interrupts, making it suitable for microcode. Finally, the system requires the same average time, and not more than twice the space, of a classical implementation, and those space requirements can be reduced to approximately classical proportions by compact list representation. Arrays of different sizes, a program stack, and hash linking are simple extensions to our system, and reference counting is found to be inferior for many applications. Key Words and Phrases: real-time, compacting, garbage collection, list processing, virtual memory, file or database management, storage management, storage
R.: Optimal Behavior of a Moving Creature in the Cellular Automata Model
- Parallel Computing Technologies. Number 3606 in LNCS, Krasnoyarsk, Springer (2005) 129 – 140 ISBN
"... Abstract. The goal of our investigation is to find automatically the best rule for a cell in the cellular automata model. The cells are either of type Obstacle, Empty or Creature. OnlyCreature can move around in the cell space and can perform one of the four actions: if the path to the next cell is ..."
Abstract
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Cited by 1 (1 self)
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Abstract. The goal of our investigation is to find automatically the best rule for a cell in the cellular automata model. The cells are either of type Obstacle, Empty or Creature. OnlyCreature can move around in the cell space and can perform one of the four actions: if the path to the next cell is blocked: turn left or right, if the path is free: move ahead and simultaneously turn left or right. The task of the creature is to cross all empty cells with a minimum number of steps. The behavior was modeled using a variable state machine represented by a state table. Input to the state table is the neighbor’s state in front of its moving direction. The goal is to find the absolutely best rule in the set of all possible rules. The search space grows exponentially with the number of states. As simulation, testing and evaluating the quality are very time consuming in software, the migration of the problem to a parallel hardware platform is a promising solution. In order to reduce the computation time, the search procedure was (1) implemented in hardware and (2) solutions which are equivalent under state permutations were not generated and (3) solutions which show or expect bad or trivial behavior were excluded as soon as possible in a preselection phase. Exactly six different five-state algorithms could be detected, which allow to cross all empty cells for all the given initial configurations. We described this model in Verilog HDL and in AHDL. A hardware synthesizing tool transforms the description into a configuration file which was loaded into a field programmable gate array (FPGA). Hardware implementation offers a significant speed up of many thousands compared to software. 1
Programmable reconfiguration of Physarum
, 901
"... Plasmodium of Physarum polycephalum is a large cell capable of solving graphtheoretic, optimization and computational geometry problems due to its unique foraging behavior. Also the plasmodium is unique biological substrate that mimics universal storage modification machines, namely the Kolmogorov-U ..."
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Plasmodium of Physarum polycephalum is a large cell capable of solving graphtheoretic, optimization and computational geometry problems due to its unique foraging behavior. Also the plasmodium is unique biological substrate that mimics universal storage modification machines, namely the Kolmogorov-Uspensky machine. In the plasmodium implementation of the storage modification machine data are represented by sources of nutrients and memory structure by protoplasmic tubes connecting the sources. In laboratory experiments and simulation we demonstrate how the plasmodium-based storage modification machine can be programmed. We show execution of the following operations with active zone (where computation occurs): merge two active zones, multiple active zone, translate active zone from one data site to another, direct active zone. Results of the paper bear two-fold value: they provide a basis for programming unconventional devices based on biological substrates and also shed light on behavioral patterns of the plasmodium. Keywords: Physarum polycephalum, Kolmogorov-Uspensky machine, pattern formation, morphogenesis, graph theory

