Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
Data parallelism occurs when same type of operations are conducted or done in parallel.It distributes the data across various different sets which are operating in parallel.In data parallelism we are performing the computation operations in synchronous form.
the amount of parallelism in the data parallelism depend only on the size of input provide to it, so we can say that the amount of parallelism in the data parallelism is directly proportional to the size of input to it.
let us understand the data parallelism with the help of an example,
let us take an example in which we are are performing an operation on a ’p’ sized array. we are just multiplying all the numbers.If we have to do this task with the sequenticial method and think it takes q units os time to perform a single operation then in the sequential method it would take;
p*q=pq
i.e; pq units of time.
But now think this by doing with the help of parallelism ,as single opeartion is taking p units of time and every units is processed in parallel so it would take around p/q units of time.So it is taking less time in parallel than in sequential.
So, we can easily say that in the data parallelism the time taken is less so the computational speed is more.
in this every programis broken down in the form of very small small tasks and the smaller tasks are assigned to processors.
In data parallelism for the every thread we are using the same model but the data provided to them is distributed and the data is shared among themselves.
data paralellisation is used in different type of fields for example, in field of science it is used like in chemistry ,biology and physics.
if we want to sequence the analysis of genome data,then it is used.
tation operations in synchronous form.
