Are array operations faster than loops?

You can do big operations on whole arrays at once, instead of tediously looping through each item. So, adding arrays (array1 + array2) is lightning-fast because numpy does the heavy lifting with optimized code behind the scenes. It's like having a speed boost for your data tasks!

What is array and array operations?

If the two operands of an infix operator are arrays, the arrays must have the same number of dimensions, and corresponding dimensions must have identical lower bounds and identical upper bounds.

What is faster than for loops?

Map is often faster than loops because it uses an optimized internal mechanism for iterating over the collection.

Why are array methods better than for loops?

We've learned loops are faster, but Array methods can be more readable when it handles some logic internally , like the creation of a new array or filtering out specific values.

What is the complexity of array operations?

Arrays. Because it takes a single step to access an item of an array via its index, or add/remove an item at the end of an array, the complexity for accessing, pushing or popping a value in an array is O(1) . Whereas, linearly searching through an array via its index, as seen before, has a complexity of O(n).

What is an array simple answer?

An array is a group of similar elements or data items of the same type collected at contiguous memory locations . In simple words, we can say that in computer programming, arrays are generally used to organize the same type of data.

Is array comprehension faster than for loop?

Because of differences in how Python implements for loops and list comprehension, list comprehensions are almost always faster than for loops when performing operations .

Are matrix operations faster than for loops?

loop vs matrix multiplication: speed vs memory. where the for loop is actually more efficient than all vectorized alternatives, as a rule of thumb I think most people would agree that MATLAB's matrix multiplication is much faster than doing the summation in a for loop .

Is array faster than string?

Memory Efficiency and Performance Char arrays offer better memory efficiency since they don't carry additional overhead from built-in string methods. Strings have a higher memory footprint due to additional metadata and the immutability requirement.

Are arrays faster than sets?

They demonstrate that arrays can be faster than sets for smaller data sizes , but sets become more efficient as the data size increases. The importance of choosing the appropriate data structure based on the specific use case to optimize performance is also discussed in this segment.

Speed and array operations (2024)

Permalink

I've often wondered about the speed of IDL compared to other languages.
Now, it is my understanding that IDL isn't really a compiled language
(please, correct me if I'm wrong). Although the IDL interpreter
"compiles" and IDL program before you run it, apparently it isn't compiled
all the way down to machine code. As such, I'm under the impression that
natively, IDL is always going to be slower than a fully compiled language
such as C or fortran. However, it seems to me that there might be case
where IDL might still be quicker. Namely, array operations. My question
is, is it possible for IDL to be faster than a fully compiled language?
Are the array operations in IDL so well optimized that adding together
gigantic arrays in IDL would actually be faster than the equivelent
for-loop style methods you would have to use in fortran or C? Or are C
and fortran always faster? Or do I completely misunderstand how these
languages work?

IDL can be "almost as fast as" a native compiled language [1,2], when
you are doing something which stays "inside C code" for a long time.
I.e. if you are using a built-in IDL primitive (like HISTOGRAM!) on
large data sets, you might approach the performance of the same
algorithm coded in C. In this case, all IDL is really doing is
packaging up the data, and handing it off to some compiled code
(written by RSI) which operates on it. This is really the same as
your own "native" compiled program would do. Of course, you'd have to
write it yourself!

When it comes to non-native code, i.e. plain old IDL code itself, this
is compiled down to the equivalent of byte-code, and interpreted by a
block of code inside IDL which is itself running natively (likely
compiled from C sources ITTVIS wrote). I.e. it is running "one step
removed" from machine code. However, often in the course of
interpreting some chunk of this byte-code (for lack of a better term),
IDL will have occasion to dump data into and collect data out of
natively-compiled "blocks" of code.

Such blocks exist for many things, like all basic array arithmetic,
and many of the functions you find in the IDL reference guide (save
the ones which are "written in IDL"). The larger the fraction of time
a given IDL program spends inside these "native blocks", the faster it
will operate. This statement is the essence of all the vectorization
and HISTOGRAM IDL optimizations you see. To maximize performance,
package up data into as large chunks as possible (within memory
limitations) and then get that data quickly into a native code block,
spending as much time in native code as possible.

In exchange for the power, flexibility, and programming simplicity
offered by the IDL language, you take a speed penalty. This is no
different from any other interpreted, higher-level language. In fact,
IDL manages to keep many simple things quite speedy, compared to the
others.

Of course, IDL is designed to be portable among different OS's first
and foremost, and to produce correct results as well. This means that
it is not, in my experience, aggressively optimized. For instance it
does not make use of extreme compiler optimizations.

There are two "escape clauses" where IDL may offer quite competitive
or even superior performance, at least compared to a single-threaded
"normal" C of FORTRAN code:

[1] On a large multi-core system, the IDL thread pool allows it to
easily spread simple calculations among many cores. There
is a setup penalty to do this, but it can truly speed up simple
operations on large data sets (array arithmetic, etc.). Of
course, you could implement this threading in your C or FORTRAN
version as well, and likely outperform IDL by a wide margin, but
multi-threaded programming is not so easy, whereas IDL makes this
trivial and "invisible".

[2] A very few IDL operations make use of the SIMD processor you
likely have hiding in your CPU. There was a large fuss about the
Altivec-awareness of IDL a few years back, around the time they
canceled and then reincarnated IDL for Mac. Interestingly, I
never heard much about IDL using SSE on Intel chips. The new SSE4
is (finally) very competitive with IBM's old Altivec unit. If
(and it's a big if) IDL makes use of these units for certain
operations, they could outperform C or FORTRAN implementations
which did not do so. Again, if you coded to the SIMD unit
yourself, you could realize these gains, likely many times over.

In practice, these cases are rarely encountered. I estimate a typical
"good" non-trivial IDL algorithm is roughly 5-40x slower than if
implemented in C. That's why I'm always amazed at these
cluster-computing IDL solutions: if speed is so important that you
want to throw a cluster at the problem, you would be better served
implementing the most costly portions of your algorithm in C as a DLM,
or as a separate process IDL communicates with. In the end though,
it's a trade-off: developer time vs. run time.

Speed and array operations (2024)

FAQs

Are array operations faster than loops? ›