Mem (computing)

Last updated

In computational complexity theory, computing efficiency, combinatorial optimization, supercomputing, computational cost (algorithmic efficiency) and other computational metrics, the mem is a measurement unit for the number of memory accesses used or needed by a process, function, instruction set, algorithm or data structure.

Contents

Example usage, when discussing processing time of a search tree node, for finding 10 × 10 Latin squares: "A typical node of the search tree probably requires about 75 mems (memory accesses) for processing, to check validity. Therefore the total running time on a modern computer would be roughly the time needed to perform 2×1020 mems." (Donald Knuth, 2011, The Art of Computer Programming , Volume 4A, p. 6).

Reducing mems as a speed and efficiency enhancement is not a linear benefit, as it trades off increases in ordinary operations costs.

PFOR compression

This optimization technique also is called PForDelta [1]

Although lossless compression methods like Rice, Golomb and PFOR are most often associated with signal processing codecs, the ability to optimize binary integers also adds relevance in reducing MEMS tradeoffs vs. operations. (See Golomb coding for details). [2]

See also

Related Research Articles

In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information. Typically, a device that performs data compression is referred to as an encoder, and one that performs the reversal of the process (decompression) as a decoder.

A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another. Distributed computing is a field of computer science that studies distributed systems.

<span class="mw-page-title-main">Genetic algorithm</span> Competitive algorithm for searching a problem space

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc.

In computing, an optimizing compiler is a compiler that tries to minimize or maximize some attributes of an executable computer program. Common requirements are to minimize a program's execution time, memory footprint, storage size, and power consumption.

<span class="mw-page-title-main">Load balancing (computing)</span> Set of techniques to improve the distribution of workloads across multiple computing resources

In computing, load balancing is the process of distributing a set of tasks over a set of resources, with the aim of making their overall processing more efficient. Load balancing can optimize the response time and avoid unevenly overloading some compute nodes while other compute nodes are left idle.

<span class="mw-page-title-main">System on a chip</span> Micro-electronic component

A system on a chip or system-on-chip is an integrated circuit that integrates most or all components of a computer or other electronic system. These components almost always include on-chip central processing unit (CPU), memory interfaces, input/output devices, input/output interfaces, and secondary storage interfaces, often alongside other components such as radio modems and a graphics processing unit (GPU) – all on a single substrate or microchip. SoCs may contain digital, and also analog, mixed-signal, and often radio frequency signal processing functions.

In computer science, algorithmic efficiency is a property of an algorithm which relates to the amount of computational resources used by the algorithm. An algorithm must be analyzed to determine its resource usage, and the efficiency of an algorithm can be measured based on the usage of different resources. Algorithmic efficiency can be thought of as analogous to engineering productivity for a repeating or continuous process.

<span class="mw-page-title-main">Parallel computing</span> Programming paradigm in which many processes are executed simultaneously

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling. As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

<span class="mw-page-title-main">Digital signal processor</span> Specialized microprocessor optimized for digital signal processing

A digital signal processor (DSP) is a specialized microprocessor chip, with its architecture optimized for the operational needs of digital signal processing. DSPs are fabricated on MOS integrated circuit chips. They are widely used in audio signal processing, telecommunications, digital image processing, radar, sonar and speech recognition systems, and in common consumer electronic devices such as mobile phones, disk drives and high-definition television (HDTV) products.

In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.

In computer science, program optimization, code optimization, or software optimization, is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power.

<span class="mw-page-title-main">Theoretical computer science</span> Subfield of computer science and mathematics

Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on mathematical aspects of computer science such as the theory of computation, lambda calculus, and type theory.

A space–time trade-off, also known as time–memory trade-off or the algorithmic space-time continuum in computer science is a case where an algorithm or program trades increased space usage with decreased time. Here, space refers to the data storage consumed in performing a given task, and time refers to the time consumed in performing a given task.

<span class="mw-page-title-main">Hardware acceleration</span> Specialized computer hardware

Hardware acceleration is the use of computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated in software running on a generic CPU can also be calculated in custom-made hardware, or in some mix of both.

<span class="mw-page-title-main">Recursion (computer science)</span> Use of functions that call themselves

In computer science, recursion is a method of solving a computational problem where the solution depends on solutions to smaller instances of the same problem. Recursion solves such recursive problems by using functions that call themselves from within their own code. The approach can be applied to many types of problems, and recursion is one of the central ideas of computer science.

The power of recursion evidently lies in the possibility of defining an infinite set of objects by a finite statement. In the same manner, an infinite number of computations can be described by a finite recursive program, even if this program contains no explicit repetitions.

In computing, computer performance is the amount of useful work accomplished by a computer system. Outside of specific contexts, computer performance is estimated in terms of accuracy, efficiency and speed of executing computer program instructions. When it comes to high computer performance, one or more of the following factors might be involved:

Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. Pointer jumping allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path. It does this by "jumping" to the end of the path computed by neighbors.

This is a list of the individual topics in Electronics, Mathematics, and Integrated Circuits that together make up the Computer Engineering field. The organization is by topic to create an effective Study Guide for this field. The contents match the full body of topics and detail information expected of a person identifying themselves as a Computer Engineering expert as laid out by the National Council of Examiners for Engineering and Surveying. It is a comprehensive list and superset of the computer engineering topics generally dealt with at any one time.

<span class="mw-page-title-main">VC-6</span> A video coding format

SMPTE ST 2117-1, informally known as VC-6, is a video coding format.

References

  1. "on compression" techniques of benchmarking and optimization using compression" (PDF). Archived from the original (PDF) on 2012-12-21. Retrieved 2014-02-13.
  2. MEMS vs. OOPS Article including compression codecs

Breaking the Wall of the Quantum Computing Hype - MemComputing, Inc.