BlockSci – A blockchain analysis platform

BlockSci Documentation

Documentation is available for the python interface library.

Additionally, a demonstration Notebook is available in the Notebooks folder.

For installation instructions, see below. More detailed documentation is coming soon. Meanwhile, feel free to contact us at


tiny-dnn is a C++11 implementation of deep learning

  • reasonably fast, without GPU
    • with TBB threading and SSE/AVX vectorization
    • 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M)
  • portable & header-only
    • Run anywhere as long as you have a compiler which supports C++11
    • Just include tiny_dnn.h and write your model in C++. There is nothing to install.
  • easy to integrate with real applications
    • no output to stdout/stderr
    • a constant throughput (simple parallelization model, no garbage collection)
    • work without throwing an exception
    • can import caffe’s model
  • simply implemented
    • be a good library for learning neural networks

uThreads: Concurrent User Threads in C++(and C)

uThreads is a concurrent library based on cooperative scheduling of user-level threads(fibers) implemented in C++. User-level threads are lightweight threads that execute on top of kernel threads to provide concurrency as well as parallelism. Kernel threads are necessary to utilize processors, but they come with the following drawbacks:

  • Each suspend/resume operation involves a kernel context switch
  • Thread preemption causes additional overhead
  • Thread priorities and advanced scheduling causes additional overhead

Cooperative user-level threads, on the other hand, provide light weight context switches and omit the additional overhead of preemption and kernel scheduling. Most Operating Systems only support a 1:1 thread mapping (1 user-level thread to 1 kernel-level thread), where multiple kernel threads execute at the same time to utilize multiple cores and provide parallelism. e.g., Linux supports only 1:1 thread mapping. There is also N:1 thread mapping, where multiple user-level threads can be mapped to a single kernel-level thread. The kernel thread is not aware of the user-level threads existence. For example, Facebook’s folly::fiber, libmill, and libtask use N:1 mapping. Having N:1 mapping means if the application blocks at the kernel level, all user-level threads are blocked and application cannot move forward. One way to address this is to only block on user level, hence, blocking user-level threads. This setting works very well with IO bound applications, however, if a user thread requires using a CPU for a while, it can block other user threads and the task is better to be executed asynchronously on another core to prevent this from happening. In order to avoid this problem, user threads can be mapped to multiple kernel-level threads. Thus, creating the third scenario with M:N or hybrid mapping. e.g., go and uC++ use M:N mapping.

uThreads supports M:N mapping of uThreads (user-level threads) over kThreads (kernel-level threads) with cooperative scheduling. kThreads can be grouped together by Clusters, and uThreads can migrate among Clusters. Figure 1 shows the structure of an application implemented using uThreads using a single ReadyQueue Scheduler. You can find the documentation here


Figure 1: uThreads Architecture

ArangoDB is designed as a native multi-model database

ArangoDB is a distributed and highly scalable database for all data models. ArangoDB is fully-certified for DC/OS including persistent primitives. Setup and maintenance of a cluster is extremely easy.

Key Features in a Nutshell


  • JOINs
  • Transactions
  • Schemaless
  • JSON Objects
  • Secondary Indexes
  • Compact Storage



mlpack is a scalable machine learning library, written in C++

mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.

As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.

mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License:LGPL, version 3.)

mlpack was originally presented at the BigLearning workshop of NIPS 2011[pdf] and later published in the Journal of Machine Learning Research [pdf]. Please cite mlpack in your work using this citation.

mlpack bindings for R are provided by the RcppMLPACK project.