the concepts needed to understand how GPUs work and can be used to speed up the
execution of some algorithms. First of all this chapter gives a brief history of
the development of Graphics card until they can be used in order to make general
-purpose computation.
+purpose computation. Then the
more sofisticated video cards, providing 2D accelerations then 3D accelerations,
then some light transforms. Video cards own their own memory to perform their
computation. From at least two dedaces, every personnal computer has a video
-card which a simple for desktop computers or which provides many accelerations
+card which is simple for desktop computers or which provides many accelerations
for game and/or graphic oriented computers. In the latter case, graphic cards
-may be more expensive than the CPU.
+may be more expensive than a CPU.
After 2000, video cards allowed to apply arithmetics operations simulatenously
on a sequence of pixels, also later called stream processing. In this case,
in order to produce a pixel color that can be displayed on a
screen. Simultaneous computations are provided by shaders which calculate
rendering effects on graphics hardware with a high degree of flexibility. These
-shaders handles the stream data with pipelines
+shaders handles the stream data with pipelines.
Some reasearchers tried to apply those operations on other data, representing
\section{Architecture of current GPUs}
-Architecure of current GPUs is constantly evolving. Nevertheless some trends
-remains true through this evolution. Processing units composing a GPU are far
-more simpler than a traditional CPU but it is much easier to integrate many
-computing units inside a GPU card than many cores inside a CPU. This is due to
-the fact that cores of a GPU a simpler than cores of a CPU. In 2012, the most
-powerful GPUs own more than 500 cores and the most powerful CPUs have 8
+Architecture \index{Architecture of a GPU} of current GPUs is constantly
+evolving. Nevertheless some trends remains true through this
+evolution. Processing units composing a GPU are far more simpler than a
+traditional CPU but it is much easier to integrate many computing units inside a
+GPU card than many cores inside a CPU. This is due to the fact that cores of a
+GPU are simpler than cores of a CPU. In 2012, the most powerful GPUs own more
+than 500 cores and the most powerful CPUs have 8
cores. Figure~\ref{ch1:fig:comparison_cpu_gpu} shows the number of cores inside
a CPU and inside a GPU. In fact, in a current NVidia GPU, there are
multiprocessors which have 32 cores (for example on Fermi cards). The core clock
On most powerful GPU cards, called Fermi, multiprocessors are called streaming
multiprocessors (SM). Each SM contains 32 cores and is able to perform 32
floating point or integer operations on 32bits numbers per clock or 16 floating
-point on 64bits number per clock. SM have their own registers, execution
+point on 64bits number per clock. SMs have their own registers, execution
pipelines and caches. On Fermi architecture, there are 64Kb shared memory + L1
cache and 32,536 32bits registers per SM. More precisely the programmer can
decide what amount of shared memory and L1 cache SM can use. The constaint is
performance optimizations such as speculative execution which roughly speaking
consists in executing a small part of code in advance even if later this work
reveals to be useless. In opposite, GPUs do not have low latency memory. In
-comparison GPUs have ridiculous cache memories. Nevertheless the architecture of GPUs is optimized for throughtput computation and it takes into account the memory latency.
+comparison GPUs have ridiculous cache memories. Nevertheless the architecture of
+GPUs is optimized for throughtput computation and it takes into account the
+memory latency.
\section{Kinds of parallelism}
Many kinds of parallelism are avaible according to the type of hardware.
-Roughtly speaking, there are three classes of parallism: instruction-level
+Roughtly speaking, there are three classes of parallelism: instruction-level
parallelism, data parallelism and task parallelism.
Instruction-level parallelism consists in re-ordering some instructions in order
-to executed some of them in parallel without changing the result of the code.
+to execute some of them in parallel without changing the result of the code.
In modern CPUs, instruction pipelines allow processor to execute instruction
-faster. With a pipeline a processor can execute multiple instruction
+faster. With a pipeline a processor can execute multiple instructions
simultaneously due to the fact that the output of a task is the input of the
next one.
\section{Memory hierarchy}
-The memory hierarchy of GPUs is different from the one of CPUs. In practice,
-there is registers, local memory, shared memory, cache memroy and global memory.
+The memory hierarchy of GPUs\index{Memory hierarchy of a GPU} is different from
+the CPUs one. In practice, there are registers, local memory, shared memory,
+cache memroy and global memory.
As previously mentioned each thread can access its own registers. It is
important to keep in mind that the number of registers per block is limited. On
recent cards, this number is limited to 64Kb per SM. Access to registers is
very fast, so when possible it is a good idea to use them.
-Likewise each thread can access local memory which in practice much slower than
-registers. In practice, local memory is automatically used by the compiler when
-all the registers are occupied. So the best idea is to optimize the use of
-registers even if this implies to reduce the number of threads per block.
+Likewise each thread can access local memory which, in practice, is much slower
+than registers. Local memory is automatically used by the compiler when all the
+registers are occupied. So the best idea is to optimize the use of registers
+even if this implies to reduce the number of threads per block.
Shared memory allows cooperation between threads of the same block. This kind
of memory is fast by it requires to be manipulated manually and its size is
