Intel(R) - Computational and Systems Biology at MIT

Managing Performance andMemory 6The chapter features different ways to obtain best performance with Intel® Math KernelLibrary (Intel® MKL): primarily, it discusses threading (see Using Intel® MKL Parallelism),then shows coding techniques and gives hardware configuration tips for improvingperformance. The chapter also discusses the Intel MKL memory management and showshow to redefine memory functions that the library uses by default.Using Intel® MKL ParallelismBeing designed for multi-threaded programming, Intel MKL is thread-safe, which meansthat Intel MKL functions work correctly during simultaneous execution by multiple threads.In particular, any chunk of threaded Intel MKL code provides access of multiple threads tothe same shared data, while permitting only one thread at any given time to access ashared piece of data. Due to thread-safety, you can call Intel MKL from multiple threadsand not worry about the functions interfering with each other.Intel MKL is threaded in a number of places:• Direct sparse solver• LAPACK— Linear equations, computational routines:- factorization: *getrf, *gbtrf, *potrf, *pptrf, *sytrf, *hetrf, *sptrf, *hptrf- solving: *gbtrs, *gttrs, *pptrs, *pbtrs, *pttrs, *sytrs, *sptrs, *hptrs,*tptrs, *tbtrs— Orthogonal factorization, computational routines:*geqrf, *ormqr, *unmqr, *ormlq, *unmlq, *ormql, *unmql, *ormrq, *unmrq— Singular Value Decomposition, computational routines: *gebrd, *bdsqr— Symmetric Eigenvalue Problems, computational routines:*sytrd, *hetrd, *sptrd, *hptrd, *steqr, *stedc6-1