added run time number for blas.

theano/misc/check_blas.py

@@ -88,14 +88,16 @@ if __name__ == "__main__":
         print """
         Some result that you can compare again. They where 10 executions of gemm in float64 with matrix of shape 2000x2000 on FC9.
 
-        Cpu tested: Xeon E5345, Xeon E5430, Xeon E5450, Core 2 E8500, Core i7 930(hyper-threads enabled)
+        Cpu tested: Xeon E5345, Xeon E5430, Xeon E5450(3Ghz), Xeon X5560(2.8Ghz, hyper-threads enabled?)
+                    Core 2 E8500, Core i7 930(2.8Ghz, hyper-threads enabled)
+                    
 
         Lib tested:
             * numpy with ATLAS from distribution(FC9) package (1 thread)
             * manually compiled numpy and ATLAS with 2 threads
             * goto with 1, 2, 4 and 8 threads.
-                          Xeon   Xeon   Xeon  Core2 i7
-        lib/nb threads    E5345  E5430  E5450 E8500 930
+                          Xeon   Xeon   Xeon  Core2 i7    Xeon
+        lib/nb threads    E5345  E5430  E5450 E8500 930   X5560
 
         numpy_FC9_atlas/1 39.2s  35.0s  30.7s 29.6s 21.5s
         goto/1            18.7s  16.1s  14.2s 13.7s 16.1s
@@ -103,6 +105,10 @@ if __name__ == "__main__":
         goto/2            9.5s   8.1s   7.1s  7.3s  8.1s
         goto/4            4.9s   4.4s   3.7s  -     4.1s
         goto/8            2.7s   2.4s   2.0s  -     4.1s
+        mkl 10.2.2.025/1                                 13.7s
+        mkl 10.2.2.025/2                                 7.6s
+        mkl 10.2.2.025/4                                 4.0s
+        mkl 10.2.2.025/8                                 2.0s
 
         Test time in float32 with cuda 3.0.14
         (cuda version 3.2RC and up are supposed to have faster gemm on the GTX4?? card)