Difference between parallel processing done by human brain and by computers I am asking a question regarding parallel processing as done by billions of Neurons inside our brain and parallel processing done by our computers in a cluster for example or even on a Graphics processor having 500 processing cores.

Recently we have seen great success in applying computers to solve complex problems applying parallel processing, such as in the field of scientific simulations, modeling, video/image processing etc.  Also due to limitations on semiconductor devices, chip makers are now forced to develop multi-core CPUs. This encourages and motivates software developers to apply parallel programming to gain the required performance, exploiting all the processing cores inside the CPU. 

While people are encouraging to solve problems using parallel programming on the computers, at the same time generally discouraging to do parallel processing inside the brain. Expert say that we must concentrate in one dimension to get best results. Why can't human brains be used to do massive parallel processing in the same way computers are doing today?
The brain is having billions of computational units called Neurons, and thus it provides an opportunity to do processing at both coarser level and at finer level. 
Why can't we use our brain to do certain jobs in parallel in the same way as computers, for example, while I am writing this question using my right hand, why cant I solve a problem in geometry at the same time, using my left hand? Using much less powerful computers having just two processing cores (vs billions in brain) I can do these two jobs of writing and solving geometry problem, in parallel by launching two separate threads. Why it is so?
There is a passage in On intelligence about the differences between parallel processing in human versus computers :


  From the dawn of the industrial revolution, people have viewed the
  brain as some sort of machine. They knew there weren't gears and cogs
  in the head, but it was the best metaphor they had. Somehow
  information entered the brain and the brain-machine determined how the
  body should react. During the computer age, the brain has been viewed
  as a particular type of machine, the programmable computer. And as we
  saw in chapter 1, AI researchers have stuck with this view, arguing
  that their lack of progress is only due to how small and slow
  computers remain compared to the human brain. Today's computers may be
  equivalent only to a cockroach brain, they say, but when we make
  bigger and faster computers they will be as intelligent as humans.
  
  There is a largely ignored problem with this brain-as-computer
  analogy. Neurons are quite slow compared to the transistors in a
  computer. A neuron collects inputs from its synapses, and combines
  these inputs together to decide when to output a spike to other
  neurons. A typical neuron can do this and reset itself in about five
  milliseconds (5 ms), or around two hundred times per second. This may
  seem fast, but a modern silicon-based computer can do one billion
  operations in a second. This means a basic computer operation is five
  million times faster than the basic operation in your brain! That is a
  very, very big difference. So how is it possible that a brain could be
  faster and more powerful than our fastest digital computers? "No
  problem," say the brain-as-computer people. "The brain is a parallel
  computer. It has billions of cells all computing at the same time.
  This parallelism vastly multiplies the processing power of the
  biological brain." 
  
  I always felt this argument was a fallacy, and a simple thought
  experiment shows why. It is called the "one hundred–step rule." A
  human can perform significant tasks in much less time than a second.
  For example, I could show you a photograph and ask you to determine if
  there is cat in the image. Your job would be to push a button if there
  is a cat, but not if you see a bear or a warthog or a turnip. This
  task is difficult or impossible for a computer to perform today, yet a
  human can do it reliably in half a second or less. But neurons are
  slow, so in that half a second, the information entering your brain
  can only traverse a chain one hundred neurons long. That is, the brain
  "computes" solutions to problems like this in one hundred steps or
  fewer, regardless of how many total neurons might be  involved. From
  the time light enters your eye to the time you press the button, a
  chain no longer than one hundred neurons could be involved. A digital
  computer attempting to solve the same problem would take billions of
  steps. One hundred computer instructions are barely enough to move a
  single character on the computer's display, let alone do something
  interesting.
  
  But if I have many millions of neurons working together, isn't that
  like a parallel computer? Not really. Brains operate in parallel and
  parallel computers operate in parallel, but that's the only thing they
  have in common. Parallel computers combine many fast computers to work
  on large problems such as computing tomorrow's weather. To predict the
  weather you have to compute the physical conditions at many points on
  the planet. Each computer can work on a different location at the same
  time. But even though there may be hundreds or even thousands of
  computers working in parallel, the individual computers still need to
  perform billions or trillions of steps to accomplish their task. The
  largest conceivable parallel computer can't do anything useful in one
  hundred steps, no matter how large or how fast.
  
  Here is an analogy. Suppose I ask you to carry one hundred stone
  blocks across a desert. You can carry one stone at a time and it takes
  a million steps to cross the desert. You figure this will take a long
  time to complete by yourself, so you recruit a hundred workers to do
  it in parallel. The task now goes a hundred times faster, but it still
  requires a minimum of a million steps to cross the desert. Hiring more
  workers— even a thousand workers— wouldn't provide any additional
  gain. No matter how many workers you hire, the problem cannot be
  solved in less time than it takes to walk a million steps. The same is
  true for parallel computers. After a point, adding more processors
  doesn't make a difference. A computer, no matter how many processors
  it might have and no matter how fast it runs, cannot "compute" the
  answer to difficult problems in one hundred steps.
  
  So how can a brain perform difficult tasks in one hundred steps that
  the largest parallel computer imaginable can't solve in a million or a
  billion steps? The answer is the brain doesn't "compute" the answers
  to problems; it retrieves the answers from memory. In essence, the
  answers were stored in memory a long time ago. It only takes a few
  steps to retrieve something from memory. Slow neurons are not only
  fast enough to do this, but they constitute the memory themselves. The
  entire cortex is a memory system. It isn't a computer at all.


The point made here is that the computing paradigm (that is, the way the whole thing works) of the brain and the computer are completely different. The computer is a Turing machine, and the brain is something else, possibly a memory system if you think that Jeff Hawking is right. Whatever it is, the brain is not a Turing machine.

To go back to your question:


  Why can't human brains be used to do massive parallel processing in
  the same way computers are doing today?


It has to do with the way the human brain works. If you assume that the brain will do any task in a parallel fashion, and the more neurons involved, the better the performance; then in order to maximize your performance you should use your whole brain. 1 task: 100% performance, 2 tasks: 50% performance, 3 tasks: 33% performance, and so on.

But if you add an "attention switching cost" to go from one task to another, then you are better off just focusing on one task where the switching cost is zero.

So you can multitask, but it won't be efficient.