Nash equilibrium mapping vs Hamiltonian dynamics vs Darwinian evolution for some social dilemma games in the thermodynamic limit

How cooperation evolves and manifests itself in the thermodynamic or infinite player limit of social dilemma games is a matter of intense speculation. Various analytical methods have been proposed to analyse the thermodynamic limit of social dilemmas. In a previous work [Chaos Solitons and fractals 135, 109762(2020)] involving one among us, two of those methods, Hamiltonian Dynamics(HD) and Nash equilibrium(NE) mapping were compared. The inconsistency and incorrectness of HD approach vis-a-vis NE mapping was brought to light. In this work we compare a third analytical method, i.e, Darwinian evolution(DE) with NE mapping and a numerical agent based approach. For completeness, we give results for HD approach as well. In contrast to HD which involves maximisation of payoffs of all individuals, in DE, payoff of a single player is maximised with respect to its nearest neighbour. While, HD utterly fails as compared to NE mapping, DE method gives a false positive for game magnetisation -- the net difference between the fraction of cooperators and defectors -- when payoffs obey the condition a+d=b+c, wherein a, d represent the diagonal elements and b, c the off diagonal elements in symmetric social dilemma games. When either a+d =/= b+c or, when one looks at average payoff per player, DE method fails much like the HD approach. NE mapping and numerical agent based method on the other hand agree really well for both game magnetisation as well as average payoff per player for the social dilemmas in question, i.e., Hawk-Dove game and Public goods game. This paper thus bring to light the inconsistency of the DE method vis-a-vis both NE mapping as well as a numerical agent based approach.Comment: 15 pages, 4 figures, 2 table

Fast and Memory Efficient Strassen’s Matrix Multiplication on GPU Cluster

Prior implementations of Strassen's matrix multiplication algorithm on GPUs traded additional workspace in the form of global memory or registers for time. Although Strassen's algorithm offers a reduction in computational complexity as compared to the classical algorithm, the memory overhead associated with the algorithm limits its practical utility. While there were past attempts at reducing the memory footprint of Strassen's algorithm by compromising parallelism, no prior implementation, to our knowledge, was able to hide the workspace requirement successfully. This thesis presents an implementation of Strassen's matrix multiplication in CUDA, titled Multi-Stage Memory Efficient Strassen (MSMES), that eliminates additional workspace requirements by reusing and recovering input matrices. MSMES organizes the steps involved in Strassen's algorithm into five stages where multiple steps in the same stage can be executed in parallel. Two additional stages are also discussed in the thesis that allows the recovery of the input matrices. Unlike previous works, MSMES has no additional memory requirements irrespective of the level of recursion of Strassen's algorithm. Experiments performed with MSMES (with the recovery stages) on NVIDIA Tesla V100 GPU and NVIDIA GTX 1660ti GPU yielded higher compute performance and lower memory requirements as compared to the NVIDIA library function for double precision matrix multiplication, cublasDgemm. In the multi-GPU adaptation of matrix multiplication, we explore the performance of a Strassen-based and a tile-based global decomposition scheme. We also checked the performance of using MSMES and cublasDgemm for performing local matrix multiplication with each of the global decomposition schemes. From the experiments, it was identified that the combination of using Strassen-Winograd decomposition with MSMES yielded the highest speedup among all the tested combinations

Emotion Based Music Player - XBeats

This paper showcases the development of an Android platform based application named XBeats which acts as a Music Player working on Image Processing fundamentals to capture, analyze and present music as per the emotion or mood of the user using this application. The Android application was developed using the Android SDK software and OpenCV software was used to implement facial recognition algorithms and cascades. The unique aspect of this project is that it focuses on facial recognition on the Android platform unlike that on Computer systems which use commonly available softwares for the same. This paper also provides comparison between use of various classification algorithms used for facial detection