英特尔® Cilk™ Plus

vectorizing with an inline function?

I attached two code files mandel1.cpp and mandel2.cpp.

mandel1.cpp has a loop with all the code in the body

mandel2.cpp has equivalent code but instead of having the code in the body it calls an inline function

Compiling with intel c++ compiler 15 with "icc  -O3 -fp-model fast=2 -xCORE-AVX2 -fma -c -S", I can vectorize mandel1.cpp but not mandel2.cpp.

Is there I way I can vectorize mandel2.cpp and still have a separate function? It seems like the optimizer ought to just be able to inline and then apply the vectorization if it can vectorize mandel1.cpp.

How to compile cilk plus runtime source with Intel® C++ Composer XE 2013

Dear all,

I want to compile cilk plus runtime source with Intel® C++ Composer XE 2013. I build the cilk plus runtime according to the directions in the "readme" file (libtoolize; aclocal; automake --add-missing; autoconf; ./configure; make; make install). But in this way, gcc is used by default.

Please, could somebody give me some guidelines in order to compile cilk plus runtime source with Intel® C++ Composer XE 2013? 

Thanks a lot for your help.

Best Regards,

Yaqiong Peng

Thread local calculation of reducers?

Hi,

I wonder how reducers work internally. So if a value is set into a reducer, does it block other threads each time a value is set?

I ask because normally I'm creating a local 'reducer', e.g. a local histogram on an image tile and on leaving the thread all the data is pushed at once into the global reducer. Just like local memory operations in OpenCL.

Intel® System Studio - Multicore Programming with Intel® Cilk™ Plus

Intel System Studio not only provides a variety of signal processing primitives via Intel® Integrated Performance Primitives (Intel® IPP), and Intel® Math Kernel Library (Intel® MKL), but also allows developing high-performance low-latency custom code (Intel C++ Compiler with Intel Cilk Plus). Since Intel Cilk Plus is built into the compiler, it can be used where it demands an efficient threading runtime in order to extract parallelism. Therefore it's possible to effectively introduce multicore parallelism even without introducing it into each of the important algorithms e.g., by employing a parallel pattern called pipeline. For custom code (e.g., code that's not reused via a library), one can rely (in addition to auto-vectorization) on an extended Array Notation incl. elemental functions (kernels) to explicitly vectorize at a higher level compared to ISA-specific intrinsic functions.
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