X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/6e645292c9053655e4e44bbebf1c1eb751a554a6..aa5e4797e8e8585b78ee799ff26314839eac0d13:/BookGPU/Chapters/chapter8/ch8.tex?ds=inline

diff --git a/BookGPU/Chapters/chapter8/ch8.tex b/BookGPU/Chapters/chapter8/ch8.tex
index a2529b1..f5c001a 100644
--- a/BookGPU/Chapters/chapter8/ch8.tex
+++ b/BookGPU/Chapters/chapter8/ch8.tex
@@ -1,6 +1,6 @@
 
-\chapterauthor{Imen Chakroun}{Universit\'e Lille 1 CNRS/LIFL, INRIA Lille Nord Europe, Cit\'e scientifique - 59655, Villeneuve d'Ascq cedex, France\\}
-\chapterauthor{Nouredine Melab}{Universit\'e Lille 1 CNRS/LIFL, INRIA Lille Nord Europe, Cit\'e scientifique - 59655, Villeneuve d'Ascq cedex, France\\}
+\chapterauthor{Imen Chakroun and Nouredine Melab}{University of Lille 1 CNRS/LIFL, INRIA Lille Nord Europe, Cit\'e scientifique, 59655 Villeneuve d'Ascq cedex, France\\}
+%\chapterauthor{Nouredine Melab}{Universit\'e Lille 1 CNRS/LIFL, INRIA Lille Nord Europe, Cit\'e scientifique - 59655, Villeneuve d'Ascq cedex, France\\}
 
 \chapter{GPU-accelerated Tree-based Exact Optimization Methods}
 \label{ch8:GPU-accelerated-tree-based-exact-optimization-methods}
@@ -140,7 +140,6 @@ The parallel evaluation of bounds \index{parallel evaluation of bounds} model, a
 \label{ch8:BB-FSP}
 
 \subsection{Definition of the Flowshop Scheduling Problem} 
-\label{ch8:LB-FSP}
 
 As a case study for our GPU-based Branch-and-Bound, we considered the NP-hard and well-known problem in the scheduling theory: the "Permutation Flow-shop Scheduling Problem" (FSP). 
 In this work, the mono-objective case is considered. The FSP aims to find the optimal schedule of n jobs on m machines so that the overall completion time of all jobs, called {\it makespan}, is minimized. 
@@ -169,7 +168,6 @@ Figure~\ref{flow-shop} illustrates a solution of a flow-shop problem instance de
 \vspace{0.3cm}
 
 \subsection{Lower Bound \index{Lower Bound} for the Flowshop Scheduling Problem}
-\label{ch8:LB-FSP}
 
 The lower bounding technique provides a lower bound (LB) for each sub-problem generated by the branching operator. The more the bound is accurate, the more it allows to eliminate not promising nodes from the search tree. Therefore, the efficiency of a B\&B algorithm depends strongly on the quality of its lower bound function. In this chapter, we use the lower bound proposed by Lenstra {\it et al.}~\cite{ch8:Lenstra_1978} for FSP, based on the Johnson's algorithm~\cite{ch8:Johnson_1954}.
 
@@ -254,11 +252,12 @@ In the following, we present how we dealt with the thread/branch divergence issu
 
 \vspace{-0.4cm}
 
-\section{Thread divergence \index{Thread divergence}}
+\section{Thread divergence}
+\label{ch8:ThreadDivergence}
 
 \subsection{The thread divergence issue}
 
-During the execution of an application on GPU, to each GPU multiprocessor is assigned one or more thread block(s) to execute. Those threads are partitioned into warps that get scheduled for execution. For each  instruction of the flow, the multiprocessor selects a warp that is ready to be run. A warp executes one common instruction at a time, so full efficiency is realized when all threads of a warp agree on their execution path. In this chapter, the G80 model, in which a warp is a pool of 32 threads, is used. If threads of a warp diverge via a data-dependent conditional branch, the warp serially executes each branch path taken. Threads that are not on the taken path are disabled, and when all paths complete, the threads converge back to the same execution path. This phenomenon is called thread/branch divergence and often causes serious performance degradations. Branch divergence occurs only within a warp; different warps execute independently regardless of whether they are executing common or disjointed code paths.
+During the execution of an application on GPU, to each GPU multiprocessor is assigned one or more thread block(s) to execute. Those threads are partitioned into warps that get scheduled for execution. For each  instruction of the flow, the multiprocessor selects a warp that is ready to be run. A warp executes one common instruction at a time, so full efficiency is realized when all threads of a warp agree on their execution path. In this chapter, the G80 model, in which a warp is a pool of 32 threads, is used. If threads of a warp diverge via a data-dependent conditional branch, the warp serially executes each branch path taken. Threads that are not on the taken path are disabled, and when all paths complete, the threads converge back to the same execution path. This phenomenon is called thread/branch divergence\index{Thread divergence} and often causes serious performance degradations. Branch divergence occurs only within a warp; different warps execute independently regardless of whether they are executing common or disjointed code paths.
 
 \vspace{0.2cm}
 
@@ -463,7 +462,6 @@ CUDA enabled devices use several memory spaces, which have different characteris
 The data access optimization challenge is to find the best mapping of the data structures of the application at hand (different sizes and access frequencies) and the GPU hierarchy of memories (different sizes and access latencies). For instance, of these different memory spaces, global memory is the most plentiful but the one with the highest access latency. On the contrary, shared memory is smaller in size but has much higher bandwidth and lower latency than the global memory.
 
 \subsection{Complexity analysis of the memory usage of the Lower Bound }
-\label{ch8:MemComplex}
 
 In this section, the characteristics of the data structures used by the lower bound function are studied in terms of sizes and access frequencies. For an efficient implementation of the LB, six data structures are required: the  matrix $PTM$ of the processing times of the jobs, the matrix of lags $LM$, the Johnson's matrix $JM$, the matrix $RM$ of the earliest starting times of jobs, the matrix $QM$ of their lowest latency times and the matrix $MM$ containing the couples of machines. The complexities of the different data structures are summarized in Table~\ref{ch8:tabMemComplex} where the columns represent respectively the name of the data structure, its size and the number of times it is accessed.
 
@@ -499,12 +497,11 @@ To reduce the computation time cost of the term $\min\limits_{(i,j)\in \jmath^2,
    MM &  $m \times (m-1)$ & $m \times (m-1)$ \\
  \hline
 \end{tabular}
- \caption{The different data structures of the $LB$ algorithm and their associated complexities in memory size and numbers of accesses. The parameters $n$, $m$ and $n'$ designate respectively the total number of jobs, the total number of machines and the number of remaining jobs to be scheduled for the sub-problems the lower bound is being computed.}
+ \caption[The different data structures of the $LB$ algorithm and their associated complexities in memory size and numbers of accesses.]{The different data structures of the $LB$ algorithm and their associated complexities in memory size and numbers of accesses. The parameters $n$, $m$ and $n'$ designate respectively the total number of jobs, the total number of machines and the number of remaining jobs to be scheduled for the sub-problems the lower bound is being computed.}
 \label{ch8:tabMemComplex}
 \end{table}
 
 \subsection{Data placement pattern of the Lower Bound on GPU}
-\label{ch8:MemComplex}
 
 This section discusses how best to map the six data structures identified above on the various kinds of memories of the GPU device.
 
@@ -534,7 +531,7 @@ $50 \times 20$ & 9.500 (9.5KB) & 9.500 (19KB) & 1.000 (1KB) & 20 (0.04KB) & 380
 $20 \times 20$ & 3.800 (3.8KB) & 3.800 (7.6KB) & 400 (0.4KB) & 20 (0.04KB) & 380 (0.76KB) \\
    \hline
   \end{tabular}
-\caption{The sizes of each data structure for the different experimented problem instances. The sizes are given in number of elements and in bytes (between brackets).}
+\caption[The sizes of each data structure for the different experimented problem instances.]{The sizes of each data structure for the different experimented problem instances. The sizes are given in number of elements and in bytes (between brackets).}
 \label{ch8:tabMemSizes}
 \end{table}
 
@@ -765,7 +762,7 @@ $20 \times $20 & 41.94 & \textbf{60.10} & 48.28 & 39.86 & 39.61 & 38.93 & 37.79
 %     \hline
 %     \hline
   \end{tabular}
- \caption{Speedup for different FSP instances and pool sizes obtained with data access optimization. $PTM$ is placed in shared memory and all others are placed in global memory.}
+ \caption[Speedup for different FSP instances and pool sizes obtained with data access optimization.]{Speedup for different FSP instances and pool sizes obtained with data access optimization. $PTM$ is placed in shared memory and all others are placed in global memory.}
 \label{ch8:PTM-on-SM}
 \end{table}
 
@@ -795,7 +792,7 @@ $20 \times $20 & 49.00 & \textbf{60.25} & 55.50 & 45.88 & 44.47 & 43.11 & 42.82
 %     \hline
 %     \hline
   \end{tabular}
- \caption{Speedup for different FSP instances and pool sizes obtained with data access optimization. 
+ \caption[Speedup for different FSP instances and pool sizes obtained with data access optimization.]{Speedup for different FSP instances and pool sizes obtained with data access optimization. 
 $JM$ is placed in shared memory and all others are placed in global memory.}
 \label{ch8:JM-on-SM}
 \end{table}		
@@ -826,7 +823,7 @@ $20 \times $20 & 53.64 & \textbf{61.47} & 59.55 & 51.39 & 47.40 & 46.53 & 46.37\
 %     \hline
 %     \hline
   \end{tabular}
- \caption{Speedup for different FSP instances and pool sizes obtained with data access optimization. $PTM$ and $JM$ are placed together in shared memory and all others are placed in global memory.}
+ \caption[Speedup for different FSP instances and pool sizes obtained with data access optimization.]{Speedup for different FSP instances and pool sizes obtained with data access optimization. $PTM$ and $JM$ are placed together in shared memory and all others are placed in global memory.}
 \label{ch8:JM-PTM-on-SM}
 \end{table}