yang_88的专栏

This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA® CUDA® GPUs. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures.

‣ Added new section C++11 Language Features, ‣ Clarified that values of const-qualified variables with builtin floating-point types cannot be used directly in device code when the Microsoft compiler is used as the host compiler, ‣ Documented the extended lambda feature, ‣ Documented that typeid, std::type_info, and dynamic_cast are only supported in host code, ‣ Documented the restrictions on trigraphs and digraphs, ‣ Clarified the conditions under which layout mismatch can occur on Windows.

Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply introducing fictitious “momentum” variables. A key to its usefulness is that Hamiltonian dynamics preserves volume, and its trajectories can thus be used to define complex mappings without the need to account for a hard-to-compute Jacobian factor — a property that can be exactly maintained even when the dynamics is approximated by discretizing time. In this review, I discuss theoretical and practical aspects of Hamiltonian Monte Carlo, and present some of its variations, including using windows of states for deciding on acceptance or rejection, computing trajectories using fast approximations, tempering during the course of a trajectory to handle isolated modes, and short-cut methods that prevent useless trajectories from taking much computation time.

An Introduction to GCC for the GNU Compilers gcc and g++