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ref: -0 tags: gpu burn stress test github cuda date: 07-13-2021 21:32 gmt revision:0 [head]

https://github.com/wilicc/gpu-burn Mult-gpu stress test.

Are your GPUs overclocked to the point of overheating / being unreliable?

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ref: bookmark-0 tags: intrinsic evolution FPGA GPU optimization algorithm genetic date: 01-27-2013 22:27 gmt revision:1 [0] [head]


  • http://evolutioninmaterio.com/ - using FPGAs in intrinsic evolution, e.g. the device is actually programmed and tested.
  • - Adrian Thompson's homepage. There are many PDFs of his work on his homepage.
  • Parallel genetic algorithms on programmable graphics hardware
    • basically deals with optimizing mutation and fitness evaluation using the parallel arcitecture of a GPU: larger populations can be evaluated at one time.
    • does not concern the intrinsic evolution of algorithms to the GPU, as in the Adrian's work.
    • uses a linear conguent generator to produce random numbers.
    • used a really simple problem: Colville minimization problem which need only search through a four-dimensional space.
  • Cellular genetic algoritms and local search for 3-SAT problem on Graphic Hardware
    • concerning SAT: satisfiabillity technique: " many practical problems, such as graph coloring, job-shop scheduling, and real-world scheduling can be represented as a SAT problem.
    • SAT-3 refers to the length of the search clause. length 3 is apparently very hard..
    • they use a combination of greedy search (flip the bit that increases the fitness the largest ammount) and random-walk via point mutations to keep the algorithm away from local minima.
    • also use cellular genetic algorithm which works better on a GPU): select the optimal neignbor, not global, individual.
    • only used a GeForce 6200 gpu, but it was still 5x faster than a p4 2.4ghz.
  • Evolution of a robot controller using cartesian genetic programming
    • cartesian programming has many advantages over traditional tree based methods - e.g. blot-free evolution & faster evolution through neutral search.
    • cartesian programming is characterized by its encoding of a graph as a string of integers that represent the functions and connections between graph nodes, and program inputs and outputs.
      • this encoding was developed in the course of evolving electronic circuits, e.g. above ?
      • can encode a non-connected graph. the genetic material that is not utilized is analogous to biological junk DNA.
    • even in converged populations, small mutations can produce large changes in phenotypic behavior.
    • in this work he only uses directed graphs - there are no cycles & an organized flow of information.
    • mentions automatically defined functions - what is this??
    • used diffusion to define the fitness values of particular locations in the map. the fewer particles there eventually were in a grid location, the higher the fitness value of the robot that managed to get there.
  • Hardware evolution: on the nature of artifically evolved circuits - doctoral dissertation.
    • because evolved circuits utilize the parasitic properties of devices, they have little tolerance of the value of components. Reverse engineering of the circuits evolved to improve tolerance is not easy.

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ref: programming-0 tags: hair rendering GPU date: 04-07-2007 22:55 gmt revision:1 [0] [head]


  • the NVidia example looks particuarly interesting & beautiful!
  • mostly hairy (hah) women in the author biography section

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ref: bookmark-0 tags: linear_algebra solution simultaneous_equations GPGPU GPU LUdecomposition clever date: 0-0-2006 0:0 revision:0 [head]