cluster_sparse_solver cause segmentation fault in mkl 11.3


   My environment: linux64, mpicxx for MVAPICH2 version 2.0b, icpc version 13.1.3 (gcc version 4.7.0 compatibility). In order not to confuse with the mkl library in icpc version 13.1.3, I put the mkl 11.3 in /home/intel.

   I use the following command:

mpic++ cluster_sparse_solverc/source/cl_solver_unsym_c.c -Wl,-rpath=/home/intel/mkl/lib/intel64 -Wl,-rpath=/home/intel/compiler/lib/intel64 -L/home/intel/mkl/lib/intel64 -L/home/intel/compiler/lib/intel64 -lmkl_intel_lp64 -lmkl_core -lmkl_intel_thread -lmkl_blacs_intelmpi_lp64 -liomp5

[Bug report] Tuple unavailable in c++14?

I am using Intel compiler 15.0.3 on OSX and facing a problem for using tuple. A simple sample code here:

#include <tuple>

using namespace std;

int main() {
    tuple<int,int,int> T{1, 2, 3};
    return 0;

If compile it with

icpc -std=c++11

It will just compile fine. But if compile with

icpc -std=c++14

It will send out error

CNR mode reporting incorrect SIMD version via KVM


We had a problem with inconsistent results across some of our grid nodes, which I thought was worth sharing. After investigation we pinned this down to two different OS configurations returning different results:

  • Baremetal windows 2008
  • Virtual windows 2008 running in KVM on RHEL

Both of the machines are identical in terms of hardware (Xeon E7-4870), which supports SSE4.1/2. At the time we were using MKL v11.1.2.

Intrinsic to down-convert all 8 elements of i64 vectors to lower/higher 8 elements of i32 vector

Is there such a thing?

I think pack/unpack intrinsics are somewhere close, but I could not understand exactly what it does.

It seems fairly basic I almost feel stupid asking this, but I would really appreciate a pointer.

I would rather up-convert using a gather instruction, but AFAIK there is no up-conversion for gathering into an epi64 vector

Any suggestions?

New article “Finite Differences on Heterogeneous Distributed Systems”

New article “Finite Differences on Heterogeneous Distributed Systems”  

exemplifies cluster implementation for finite differences. It also describes an approach for static load balancing to deal with the compute imbalance of heterogeneous distributed systems.

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