node-mock-fs

configurable mock for Node.js module fs

The "mock-fs" module allows Node's built-in "fs" module to be backed temporarily by an in-memory, mock file system. This lets you run tests against a set of mock files and directories instead of lugging around a bunch of test fixtures.

libslepc-real3.19t64

Scalable Library for Eigenvalue Problem Computations

SLEPc is a software library for the solution of large scale sparse eigenvalue problems on parallel computers. It is an extension of PETSc and can be used for either standard or generalized eigenproblems, with real or complex arithmetic. It can also be used for computing a partial SVD of a large, sparse, rectangular matrix.

libnutclient2

network UPS tools - new client library

Network UPS Tools (NUT) is a client/server monitoring system that allows computers to share uninterruptible power supply (UPS) and power distribution unit (PDU) hardware. Clients access the hardware through the server, and are notified whenever the power status changes.

libnutscan2

network UPS tools - scanner library

Network UPS Tools (NUT) is a client/server monitoring system that allows computers to share uninterruptible power supply (UPS) and power distribution unit (PDU) hardware. Clients access the hardware through the server, and are notified whenever the power status changes.

libslepc64-complex3.19t64

Scalable Library for Eigenvalue Problem Computations (64-bit)

SLEPc is a software library for the solution of large scale sparse eigenvalue problems on parallel computers. It is an extension of PETSc and can be used for either standard or generalized eigenproblems, with real or complex arithmetic. It can also be used for computing a partial SVD of a large, sparse, rectangular matrix.

libslepc64-real3.19t64

Scalable Library for Eigenvalue Problem Computations (64-bit)

SLEPc is a software library for the solution of large scale sparse eigenvalue problems on parallel computers. It is an extension of PETSc and can be used for either standard or generalized eigenproblems, with real or complex arithmetic. It can also be used for computing a partial SVD of a large, sparse, rectangular matrix.