4 projects tagged "mapreduce"
Updated 15 Apr 2013
jug
| Pop | 190.11 |
|---|---|
| Vit | 25.74 |
Jug is a task-based parallelism framework. Jug allows you to write code that is broken up into tasks and run different tasks on different processors. It uses the filesystem to communicate between processes and works correctly over NFS, so you can coordinate processes on different machines. Jug is a pure Python implementation and should work on any platform that can run Python.
Updated 25 Oct 2012
dispy
| Pop | 76.95 |
|---|---|
| Vit | 2.47 |
dispy is a Python framework for parallel execution of computations by distributing them across multiple processors in a single machine (SMP), or among many machines in a cluster or grid. The computations can be standalone programs or Python functions. dispy is well suited for the data parallel (SIMD) paradigm where a computation is evaluated with different (large) datasets independently (similar to Hadoop, MapReduce, Parallel Python). dispy features include automatic distribution of dependencies (files, Python functions, classes, modules), client-side and server-side fault recovery, scheduling of computations to specific nodes, encryption for security, sharing of computation resources if desired, and more.
Updated 03 Nov 2011
Plasma
| Pop | 64.10 |
|---|---|
| Vit | 1.94 |
Plasma implements the map/reduce framework on a compute cluster. It has its own distributed filesystem, PlasmaFS, which is transactional (ACID), reliable, and fast, and which provides a complete set of file operations. PlasmaFS can be accessed via an RPC protocol or via NFS (i.e., it is mountable). Additionally, there is a key/value database on top of PlasmaFS.
Updated 30 Jun 2010
PyMW
| Pop | 48.04 |
|---|---|
| Vit | 1.00 |
PyMW is a Python module for parallel master-worker computing in a variety of environments. With the PyMW module, users can write a single program that scales from multicore machines to global computing platforms.
