Loonloon's repositories
bionmf-gpu
NMF-mGPU web site:
Block-Matrix-Multiplication-OpenMP
Implementation of block matrix multiplication using OpenMP and comparison with non-block parallel and sequentional implementation
clqueue
Implementing a Job Scheduler for GPUs in OpenCL
Deep-Metric-Learning-CVPR16
Main repository for Deep Metric Learning via Lifted Structured Feature Embedding
FasterRCNN-Encapsulation-Cplusplus
Encapsulation C++ version of FasterRCNN
fhe
Fully Homomorphic Encryption
hcrypt
Homomorphic Encryption Library
jlof
Java implementation of Local Outlier Factor algorithm
multicl
Mutli-platform OpenCL with automatic device scheduling
nonMaximumSuppression
非极大值抑制，包含了matlab，c,，c++，3种实现的代码，完美运行。并带c++，Matlab测试demo。所有程序都有详细的注释。GOOD LUCK!
oblivious
Oblivious Memory Access under Fully Homomorphic Encryption
OCL_Visualiser
Visualisation tool for OpenCL programs with additonal cache simulator under development
OpenCL-FPGA
Explores OpenCL on Xilinx's FPGA.
OpenCLFFTConvolution
FFT based convolution using OpenCL
Parallel-Programming-with-Python
Parallel Programming with Python简体中文版
parallel_programs
Some sample MPI Programs and Benchmark
ParallelSparseMatrixFactorization
Sparse Matrix Factorization (SMF) is a key component in many machine learning problems and there exist a verity a applications in real-world problems such as recommendation systems, estimating missing values, gene expression modeling, intelligent tutoring systems (ITSs), etc. There are different approaches to tackle with SMF rooted in linear algebra and probability theory. In this project, given an incomplete binary matrix of students’ performances over a set of questions, estimating the probability of success or fail over unanswered questions is of interest. This problem is formulated using Maximum Likelihood Estimation (MLE) which leads to a biconvex optimization problem (this formulation is based on SPARFA [4]). The resulting optimization problem is a hard problem to deal with due to the existence of many local minima. On the other hand, when the size of the matrix of students’ performances increase, the existing algorithms are not successful; therefore, an efficient algorithm is required to solve this problem for large matrices. In this project, a parallel algorithm (i.e., a parallel version of SPARFA) is developed to solve the biconvex optimization problem and tested via a number of generated matrices. Keywords: parallel non-convex optimization, matrix factorization, sparse factor analysis 1 Introduction Educational systems have witnessed a substantial transition from traditional educational methods mainly using text books, lectures, etc. to newly developed systems which are artificial intelligent- based systems and personally tailored to the learners [4]. Personalized Learning Systems (PLSs) and Intelligent Tutoring Systems (ITSs) are two more well-known instances of such recently developed educational systems. PLSs take into account learners’ individual characteristics then customize the learning experience to the learners’ current situation and needs [2]. As computerized learning environments, ITSs model and track student learning states [1, 6, 7]. Latent Factor Model and Bayesian Knowledge Tracing are main classes in ITSs [3]. These new approaches encompass computational models from different disciplines including cognitive and learning sciences, education, 1 computational linguistics, artificial intelligence, operations research, and other fields. More details can be found in [1, 4–6]. Recently, [4] developed a new machine learning-based model for learning analytics, which approximate a students knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. This model calculates the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each questions intrinsic difficulty [4]. They proposed a bi-convex maximum-likelihood-based solution to the resulting SPARse Factor Analysis (SPARFA) problem. However, the scalability of SPARFA when the number of questions and students significantly increase has not been studied yet.
sgx-reencrypt
PoC of an SGX enclave performing symmetric reencryption
shadowsocks
backup of https://github.com/shadowsocks/shadowsocks
skelcl
SkelCL is a library providing high-level abstractions for alleviated programming of modern parallel heterogeneous systems. SkelCL is a research project currently developed at the research group parallel and distributed systems at University of Münster which is located in Germany.