As GPUs have their own memory, the first step consists of allocating memory on
-the GPU. A call to \texttt{cudaMalloc}\index{CUDA functions!cudaMalloc}
-allocates memory on the GPU. The second parameter represents the size of the
-allocated variables, this size is expressed in bits.
-
+the GPU. A call to \texttt{cudaMalloc}\index{CUDA functions!cudaMalloc}
+allocates memory on the GPU. {\bf REREAD The first parameter of this function is a pointer
+on a memory on the device, i.e. the GPU.} The second parameter represents the
+size of the allocated variables, this size is expressed in bits.
+\pagebreak
\lstinputlisting[label=ch2:lst:ex1,caption=simple example]{Chapters/chapter2/ex1.cu}
In this example, we want to compare the execution time of the additions of two
arrays in CPU and GPU. So for both these operations, a timer is created to
-measure the time. CUDA proposes to manipulate timers quite easily. The first
+measure the time. CUDA manipulates timers quite easily. The first
step is to create the timer\index{CUDA functions!timer}, then to start it, and at
the end to stop it. For each of these operations a dedicated function is used.
the dimension of blocks of threads must be chosen carefully. In our example, only one dimension is
used. Then using the notation \texttt{.x}, we can access the first dimension
(\texttt{.y} and \texttt{.z}, respectively allow access to the second and
-third dimension). The variable \texttt{blockDim}\index{CUDA keywords!blockDim}
+third dimensions). The variable \texttt{blockDim}\index{CUDA keywords!blockDim}
gives the size of each block.
\section{Second example: using CUBLAS \index{CUBLAS}}
\label{ch2:2ex}
-The Basic Linear Algebra Subprograms (BLAS) allows programmers to use efficient
+The Basic Linear Algebra Subprograms (BLAS) allow programmers to use efficient
routines for basic linear operations. Those routines are heavily used in many
scientific applications and are optimized for vector operations, matrix-vector
operations, and matrix-matrix
operations~\cite{ch2:journals/ijhpca/Dongarra02}. Some of those operations seem
-to be easy to implement with CUDA. Nevertheless, as soon as a reduction is
+to be easy to implement with CUDA; however, as soon as a reduction is
needed, implementing an efficient reduction routine with CUDA is far from being
simple. Roughly speaking, a reduction operation\index{reduction operation} is an
operation which combines all the elements of an array and extracts a number
result of the multiplication of $A \times B$ is $C$. The
element \texttt{C[i*size+j]} is computed as follows:
\begin{equation}
-C[size*i+j]=\sum_{k=0}^{size-1} A[size*i+k]*B[size*k+j];
+C[size*i+j]=\sum_{k=0}^{size-1} A[size*i+k]*B[size*k+j].
\end{equation}
In Listing~\ref{ch2:lst:ex3}, the CPU computation is performed using 3 loops,
