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Page 561
of processors in a single chip are collectively known as
heterogeneous multicore
processors. An example of a heterogeneous multicore processor is the line of IXP
network processors originally introduced by Intel in 2000 and updated regularly
with the latest technology. The network processors typically contain a single gener-
al purpose control core (for instance, an ARM processor running Linux) and many
tens of highly specialized stream processors that are really good at processing net-
work packets and not much else. They are commonly used in network equipment,
such as routers and firewalls. To route network packets you probably do not need
floating-point operations much, so in most models the stream processors do not
have a floating-point unit at all. On the other hand, high-speed networking is high-
ly dependent on fast access to memory (to read packet data) and the stream proc-
essors have special hardware to make this possible.
In the previous examples, the systems were clearly heterogeneous. The stream
processors and the control processors on the IXPs are completely different beasts
with different instruction sets. The same is true for the GPU and the general-pur-
pose cores. However, it is also possible to introduce heterogeneity while main-
taining the same instruction set. For instance, a CPU can have a small number of
‘‘big’’ cores, with deep pipelines and possibly high clock speeds, and a larger num-
ber of ‘‘little’’ cores that are simpler, less powerful, and perhaps run at lower fre-
quencies. The powerful cores are needed for running code that requires fast
sequential processing while the little cores are useful for tasks that can be executed
efficiently in parallel. An example of a heterogeneous architecture along these lines
is ARM’s big.LITTLE processor family.
Programming with Multiple Cores
As has often happened in the past, the hardware is way ahead of the software.
While multicore chips are here now, our ability to write applications for them is
not. Current programming languages are poorly suited for writing highly parallel
programs and good compilers and debugging tools are scarce on the ground. Few
programmers have had any experience with parallel programming and most know
little about dividing work into multiple packages that can run in parallel.
chronization, eliminating race conditions, and deadlock avoidance are such stuff as
really bad dreams are made of, but unfortunately performance suffers horribly if
they are not handled well. Semaphores are not the answer.
Beyond these startup problems, it is far from obvious what kind of application
really needs hundreds, let alone thousands, of cores—especially in home environ-
ments. In large server farms, on the other hand, there is often plenty of work for
large numbers of cores. For instance, a popular server may easily use a different
core for each client request. Similarly, the cloud providers discussed in the previ-
ous chapter can soak up the cores to provide a large number of virtual machines to
rent out to clients looking for on-demand computing power.

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