suite

[book_gpu.git] / BookGPU / Chapters / chapter1 / ch1.tex

diff --git a/BookGPU/Chapters/chapter1/ch1.tex b/BookGPU/Chapters/chapter1/ch1.tex
index ec639cb7cd56676871aa07e17c28cf74c05f23c8..88c9361596ccac7d0783bc8958e268afd9550834 100755 (executable)
--- a/BookGPU/Chapters/chapter1/ch1.tex
+++ b/BookGPU/Chapters/chapter1/ch1.tex
@@ -100,7 +100,7 @@ get maximum performance.
 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
 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
 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


@@ -120,7 +120,9 @@ through  the  use  of  cache  memories. Moreover,  nowadays  CPUs  perform  many
 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
 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.

 
 
 
 
 
 


@@ -146,13 +148,13 @@ computation of other tasks.
 \section{Kinds of parallelism}
 
 Many  kinds  of parallelism  are  avaible according  to  the  type of  hardware.
 \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
 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
 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.
 
 simultaneously due  to the fact that  the output of a  task is the  input of the
 next one.