cpucycles

command-line tool for counting CPU cycles (libcpucycles)

libcpucycles provides a simple API to access hardware precise timers to understand and improve software performance.

gcc-11-multilib-mips64-linux-gnuabi64

GNU C compiler (multilib support) (cross compiler for mips64 architecture)

This is the GNU C compiler, a fairly portable optimizing compiler for C.

libghc-concurrent-extra-prof

Extra concurrency primitives; profiling libraries

The @concurrent-extra@ package offers among other things the following selection of synchronisation primitives:

gobjc-11-aarch64-linux-gnu

GNU Objective-C compiler

This is the GNU Objective-C compiler, which compiles Objective-C on platforms supported by the gcc compiler. It uses the gcc backend to generate optimized code.

libgo19-alpha-cross

Runtime library for GNU Go applications

Library needed for GNU Go applications linked against the shared library.

libstopt4

library for stochastic optimization problems (shared library)

The STochastic OPTimization library (StOpt) aims at providing tools in C++ for solving some stochastic optimization problems encountered in finance or in the industry. Different methods are available: - dynamic programming methods based on Monte Carlo with regressions (global, local, kernel and sparse regressors), for underlying states following some uncontrolled Stochastic Differential Equations; - dynamic programming with a representation of uncertainties with a tree: transition problems are here solved by some discretizations of the commands, resolution of LP with cut representation of the Bellman values; - Semi-Lagrangian methods for Hamilton Jacobi Bellman general equations for underlying states following some controlled Stochastic Differential Equations; - Stochastic Dual Dynamic Programming methods to deal with stochastic stock management problems in high dimension. Uncertainties can be given by Monte Carlo and can be represented by a state with a finite number of values (tree); - Some branching nesting methods to solve very high dimensional non linear PDEs and some appearing in HJB problems. Besides some methods are provided to solve by Monte Carlo some problems where the underlying stochastic state is controlled. For each method, a framework is provided to optimize the problem and then simulate it out of the sample using the optimal commands previously computed. Parallelization methods based on OpenMP and MPI are provided in this framework permitting to solve high dimensional problems on clusters. The library should be flexible enough to be used at different levels depending on the user's willingness.