.. title: PAR Class 7, Wed 2018-02-28
.. slug: class07
.. date: 2018-02-27
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Term project progress
---------------------

How's this going?   Send me a progress report.


Parallel programs
----------------------

/parallel-class/cuda/matmul2.cu plays with matrix multiplication of two random 1000x1000 matrices, and with managed memory.

#. It shows a really quick way to use OpenMP, which has a 10x speedup.

#. It shows a really quick way to use CUDA, which has a 15x speedup.   This just uses one thread block per input matrix A row, and one thread per output element.   That's 1M threads.  The data is read from managed memory.  Note how easy it is.   There is no storing tiles into fast shared memory.

#. It multiplies the matrices on the host, and compares reading them from normal memory and from managed memory.   The latter is 2.5x slower.    Dunno why.

#. matmul2 also shows some of my utility functions and macros.

   a. InitCUDA prints a description of the GPU etc.
   #. cout << PRINTC(expr) prints an expression's name and then its value and a comma.
   #. PRINTN is like PRINTC but ends with endl.
   #. TIME(expr) evals an expression then prints its name and total and delta

      i. CPU time,
      #. elapsed time,
      #. their ratio.

   #. CT evals and prints the elapsed time of a CUDA kernel.
   #. Later I may add new tests to this.

#. /parallel-class/cuda/checksum.cc shows a significant digits problem when
   you add many small numbers.

#. sum_reduction.cu is Stanford's program.

#. sum_reduction2.cu is my modification to use managed memory.

   Note how both sum_reduction and sum_reduction2 give different answers
   for the serial and the parallel computation.  That is bad.

#. sum_reduction3.cu is a mod to try to find the problem.  One problem is
   insufficient precision in the sum.  Using double works.  However there
   might be other problems.


      

Computer factoid
----------------

Unrelated to this course, but perhaps interesting:

All compute servers have for decades had a microprocessor frontend that controls the boot process.   The current iteration is called an
`IPMI <https://en.wikipedia.org/wiki/Intelligent_Platform_Management_Interface>`_.   It has a separate ethernet port, and would allow remote bios configs and booting.   On parallel, that port is not connected (I don't trust the security).
      
CUDA Doc
--------

The start of Nvidia's `Parallel thread execution <https://docs.nvidia.com/cuda/parallel-thread-execution/index.html>`_ has useful info.

This is one of a batch of `CUDA docs <https://docs.nvidia.com/cuda/>`_.  Browse as you wish.


Stanford lectures
-----------------

All the lectures and associated files are on geoxeon and parallel.ecse at /parallel-class/stanford/ .

They are also online `here <https://wrf.ecse.rpi.edu/Teaching/parallel-s2018/files/stanford/>`_.

`Lecture 6 parallel patterns 1 <../../files/stanford/lectures/lecture_6/parallel_patterns_1.pdf>`_ presents some paradigms of parallel programming.   These are generally useful building blocks for parallel algorithms.


Thrust
------

Stanford lecture 8: Thrust. This is a functional frontend to various backends, such as CUDA.

Programs are in /parallel-class/thrust/ .


IBM's quantum computer
----------------------

As mentioned by Narayanaswami Chandrasekhar last week.

#. https://en.wikipedia.org/wiki/IBM_Quantum_Experience

#. https://techcrunch.com/2017/11/10/ibm-passes-major-milestone-with-20-and-50-qubit-quantum-computers-as-a-service/

#. https://www.technologyreview.com/s/609451/ibm-raises-the-bar-with-a-50-qubit-quantum-computer/

#. https://www.research.ibm.com/ibm-q/ - points to lots of info, e.g., QISKit on github,
   beginners guide, etc.
   

You to do:  learn this and present it to me in class.