This future demands better programming techniques to write faster code. To illustrate that point, the MIT researchers wrote a simple Python 2 program that multiplies two 4,096-by-4,096 matrices. They used an Intel Xeon processor with 2.9-GHz 18-core CPU and shared 25-mebibyte L3-cache, running Fedora 22 and version 4.0.4 of the Linux kernel.
for i in xrange(4096):
for j in xrange(4096):
for k in xrange(4096):
C[j] += A[k] * B[k][j]
The code, they say, takes seven hours to compute the matrix product, or nine hours if you use Python 3. Better performance can be achieved by using a more efficient programming language, with Java resulting in a 10.8x speedup and C (v3) producing an additional 4.4x increase for a 47x improvement in execution time.
Beyond programming language gains, exploiting specific hardware features can make the code run 1300x faster still. By parallelizing the code to run on all 18 of the available processing cores, optimizing for processor memory hierarchy, vectorizing the code, and using Intel's Advanced Vector Extensions, the seven hour number crunching task can be reduced to 0.41s, or 60,000x faster than the original Python code.
