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Rust function as slow as its python counterpart

I am trying to speed up Python programs using Rust, a language in which I am a total beginner. I wrote a function that counts the occurrences of each possible string of length n within a larger string. For instance, if the main string is "AAAAT" and n=3, the outcome would be a hashmap {"AAA":2,"AAT":1}. I use pyo3 to call the Rust function from Python. The code of the Rust function is:
fn count_nmers(seq: &str, n: usize) -> PyResult<HashMap<&str,u64>> {
let mut current_pos: usize = 0;
let mut counts: HashMap<&str,u64> = HashMap::new();
while current_pos+n <= seq.len() {
//print!("{}\n", &seq[current_pos..current_pos+n]);
match counts.get(&seq[current_pos..current_pos+n]) {
Some(repeats) => counts.insert(&seq[current_pos..current_pos+n],repeats+1),
None => counts.insert(&seq[current_pos..current_pos+n],1)
};
current_pos +=1;
}
//print!("{:?}",counts)
Ok(counts)
}
When I use small values for n (n<10), Rust is about an order of magnitude faster than Python, but as the length of n increases, the gap tends to zero with both functions having the same speed by n=200. (see graph)
Times to count for different n-mer lengths (Python black, rust red)
I must be doing something wrong with the strings, but I can't find the mistake.
The python code is:
def nmer_freq_table(sequence,nmer_length=6):
nmer_dict=dict()
for nmer in seq_win(sequence,window_size=nmer_length):
if str(nmer) in nmer_dict.keys():
nmer_dict[str(nmer)]=nmer_dict[str(nmer)]+1
else:
nmer_dict[str(nmer)]=1
return nmer_dict
def seq_win(seq,window_size=2):
length=len(seq)
i=0
while i+window_size <= length:
yield seq[i:i+window_size]
i+=1

You are computing hash function multiple times, this may matter for large n values. Try using entry function instead of manual inserts:
while current_pos+n <= seq.len() {
let en = counts.entry(&seq[current_pos..current_pos+n]).or_default();
*en += 1;
current_pos +=1;
}
Complete code here
Next, make sure you are running --release compiled code like cargo run --release.
And one more thing to take in mind is discussed here, Rust may use non-optimal hash function for your case which you can change.
And finally, on large data, most of time is spent in HashMap/dict internals which are not a python, but compiled code. So don't expect it to scale well.

Could it be because as n gets larger the number of iterations through the loop gets smaller?
Fewer iterations through the loop would reduce the performance gain seen by using Rust. I'm sure there is a small per function call performance cost for transition/marshaling to Rust from Python. This would explain how eventually the performance from pure Python and Python/Rust becomes the same.

For loop vs Numpy vectorization computation time

I was randomly comparing the computation times of an explicit for-loop with vectorized implementation in numpy. I ran exactly 1 million iterations and found some astounding differences. For-loop took about 646ms while the np.exp() function computed the same result in less than 20ms.
import time
import math
import numpy as np
iter = 1000000
x = np.zeros((iter,1))
v = np.random.randn(iter,1)
before = time.time()
for i in range(iter):
x[i] = math.exp(v[i])
after = time.time()
print(x)
print("Non vectorized= " + str((after-before)*1000) + "ms")
before = time.time()
x = np.exp(v)
after = time.time()
print(x)
print("Vectorized= " + str((after-before)*1000) + "ms")
The result I got:
[[0.9256753 ]
[1.2529006 ]
[3.47384978]
...
[1.14945181]
[0.80263805]
[1.1938528 ]]
Non vectorized= 646.1577415466309ms
[[0.9256753 ]
[1.2529006 ]
[3.47384978]
...
[1.14945181]
[0.80263805]
[1.1938528 ]]
Vectorized= 19.547224044799805ms
My questions are:
What exactly is happening in the second case? The first one is using
an explicit for-loop and thus the computation time is justified.
What is happening "behind the scenes" in the second case?
How can one implement such computations (second case) without using numpy (in plain Python)?

What is happening is that NumPy is calling high quality numerical libraries (BLAS for instance) which are very good at vector arithmetic.
I imagine you could specifically call the exact libraries used by NumPy, however, NumPy would likely know best which to use.

NumPy is a Python wrapper over libraries and code written in C. This is a large part of the efficiency of NumPy. C code compiles directly to instructions which are executed by your processor or GPU. On the other hand, Python code must be interpreted as it executes. Despite the ever increasing speed we can get from interpreted languages with advances like Just In Time Compilers, for some tasks they will never be able to approach the speed of compiled languages.

It comes down to the fact that Python does not have direct access to the hardware level.
Python can't use the SIMD (Single instruction, multiple data) assembly instructions that most modern CPU's and GPU's have. These SIMD instruction allow a single operation to execute on a vector of data all at once (within a single clock cycle) at the hardware level.
NumPy on the other hand has functions built in C, and C is a language capable of running SIMD instructions. Therefore NumPy can take advantage of the vectorization hardware in your processor.

My python codes in general are very slow, is this normal?

I recently began self-learning python, and have been using this language for an online course in algorithms. For some reason, many of my codes I created for this course are very slow (relatively to C/C++ Matlab codes I have created in the past), and I'm starting to worry that I am not using python properly.
Here is a simple python and matlab code to compare their speed.
MATLAB
for i = 1:100000000
a = 1 + 1
end
Python
for i in list(range(0, 100000000)):
a=1 + 1
The matlab code takes about 0.3 second, and the python code takes about 7 seconds. Is this normal? My python codes for much complex problems are very slow. For example, as a HW assignment, I'm running depth first search on a graph with about 900000 nodes, and this is taking forever. Thank you.

Performance is not an explicit design goal of Python:
Don’t fret too much about performance--plan to optimize later when
needed.
That's one of the reasons why Python integrated with a lot of high performance calculating backend engines, such as numpy, OpenBLAS and even CUDA, just to name a few.
The best way to go foreward if you want to increase performance is to let high-performance libraries do the heavy lifting for you. Optimizing loops within Python (by using xrange instead of range in Python 2.7) won't get you very dramatic results.
Here is a bit of code that compares different approaches:
Your original list(range())
The suggestes use of xrange()
Leaving the i out
Using numpy to do the addition using numpy array's (vector addition)
Using CUDA to do vector addition on the GPU
Code:
import timeit
import matplotlib.pyplot as mplplt
iter = 100
testcode = [
"for i in list(range(1000000)): a = 1+1",
"for i in xrange(1000000): a = 1+1",
"for _ in xrange(1000000): a = 1+1",
"import numpy; one = numpy.ones(1000000); a = one+one",
"import pycuda.gpuarray as gpuarray; import pycuda.driver as cuda; import pycuda.autoinit; import numpy;" \
"one_gpu = gpuarray.GPUArray((1000000),numpy.int16); one_gpu.fill(1); a = (one_gpu+one_gpu).get()"
]
labels = ["list(range())", "i in xrange()", "_ in xrange()", "numpy", "numpy and CUDA"]
timings = [timeit.timeit(t, number=iter) for t in testcode]
print labels, timings
label_idx = range(len(labels))
mplplt.bar(label_idx, timings)
mplplt.xticks(label_idx, labels)
mplplt.ylabel('Execution time (sec)')
mplplt.title('Timing of integer addition in python 2.7\n(smaller value is better performance)')
mplplt.show()
Results (graph) ran on Python 2.7.13 on OSX:
The reason that Numpy performs faster than the CUDA solution is that the overhead of using CUDA does not beat the efficiency of Python+Numpy. For larger, floating point calculations, CUDA does even better than Numpy.
Note that the Numpy solution performs more that 80 times faster than your original solution. If your timings are correct, this would even be faster than Matlab...
A final note on DFS (Depth-afirst-Search): here is an interesting article on DFS in Python.

Try using xrange instead of range.
The difference between them is that **xrange** generates the values as you use them instead of range, which tries to generate a static list at runtime.

Unfortunately, python's amazing flexibility and ease comes at the cost of being slow. And also, for such large values of iteration, I suggest using itertools module as it has faster caching.
The xrange is a good solution however if you want to iterate over dictionaries and such, it's better to use itertools as in that, you can iterate over any type of sequence object.

Can Go really be that much faster than Python?

I think I may have implemented this incorrectly because the results do not make sense. I have a Go program that counts to 1000000000:
package main
import (
"fmt"
)
func main() {
for i := 0; i < 1000000000; i++ {}
fmt.Println("Done")
}
It finishes in less than a second. On the other hand I have a Python script:
x = 0
while x < 1000000000:
x+=1
print 'Done'
It finishes in a few minutes.
Why is the Go version so much faster? Are they both counting up to 1000000000 or am I missing something?

One billion is not a very big number. Any reasonably modern machine should be able to do this in a few seconds at most, if it's able to do the work with native types. I verified this by writing an equivalent C program, reading the assembly to make sure that it actually was doing addition, and timing it (it completes in about 1.8 seconds on my machine).
Python, however, doesn't have a concept of natively typed variables (or meaningful type annotations at all), so it has to do hundreds of times as much work in this case. In short, the answer to your headline question is "yes". Go really can be that much faster than Python, even without any kind of compiler trickery like optimizing away a side-effect-free loop.

pypy actually does an impressive job of speeding up this loop
def main():
x = 0
while x < 1000000000:
x+=1
if __name__ == "__main__":
s=time.time()
main()
print time.time() - s
$ python count.py
44.221405983
$ pypy count.py
1.03511095047
~97% speedup!
Clarification for 3 people who didn't "get it". The Python language itself isn't slow. The CPython implementation is a relatively straight forward way of running the code. Pypy is another implementation of the language that does many tricky (especiallt the JIT) things that can make enormous differences. Directly answering the question in the title - Go isn't "that much" faster than Python, Go is that much faster than CPython.
Having said that, the code samples aren't really doing the same thing. Python needs to instantiate 1000000000 of its int objects. Go is just incrementing one memory location.

This scenario will highly favor decent natively-compiled statically-typed languages. Natively compiled statically-typed languages are capable of emitting a very trivial loop of say, 4-6 CPU opcodes that utilizes simple check-condition for termination. This loop has effectively zero branch prediction misses and can be effectively thought of as performing an increment every CPU cycle (this isn't entirely true, but..)
Python implementations have to do significantly more work, primarily due to the dynamic typing. Python must make several different calls (internal and external) just to add two ints together. In Python it must call __add__ (it is effectively i = i.__add__(1), but this syntax will only work in Python 3.x), which in turn has to check the type of the value passed (to make sure it is an int), then it adds the integer values (extracting them from both of the objects), and then the new integer value is wrapped up again in a new object. Finally it re-assigns the new object to the local variable. That's significantly more work than a single opcode to increment, and doesn't even address the loop itself - by comparison, the Go/native version is likely only incrementing a register by side-effect.
Java will fair much better in a trivial benchmark like this and will likely be fairly close to Go; the JIT and static-typing of the counter variable can ensure this (it uses a special integer add JVM instruction). Once again, Python has no such advantage. Now, there are some implementations like PyPy/RPython, which run a static-typing phase and should fare much better than CPython here ..

You've got two things at work here. The first of which is that Go is compiled to machine code and run directly on the CPU while Python is compiled to bytecode run against a (particularly slow) VM.
The second, and more significant, thing impacting performance is that the semantics of the two programs are actually significantly different. The Go version makes a "box" called "x" that holds a number and increments that by 1 on each pass through the program. The Python version actually has to create a new "box" (int object) on each cycle (and, eventually, has to throw them away). We can demonstrate this by modifying your programs slightly:
package main
import (
"fmt"
)
func main() {
for i := 0; i < 10; i++ {
fmt.Printf("%d %p\n", i, &i)
}
}
...and:
x = 0;
while x < 10:
x += 1
print x, id(x)
This is because Go, due to it's C roots, takes a variable name to refer to a place, where Python takes variable names to refer to things. Since an integer is considered a unique, immutable entity in python, we must constantly make new ones. Python should be slower than Go but you've picked a worst-case scenario - in the Benchmarks Game, we see go being, on average, about 25x times faster (100x in the worst case).
You've probably read that, if your Python programs are too slow, you can speed them up by moving things into C. Fortunately, in this case, somebody's already done this for you. If you rewrite your empty loop to use xrange() like so:
for x in xrange(1000000000):
pass
print "Done."
...you'll see it run about twice as fast. If you find loop counters to actually be a major bottleneck in your program, it might be time to investigate a new way of solving the problem.

#troq
I'm a little late to the party but I'd say the answer is yes and no. As #gnibbler pointed out, CPython is slower in the simple implementation but pypy is jit compiled for much faster code when you need it.
If you're doing numeric processing with CPython most will do it with numpy resulting in fast operations on arrays and matrices. Recently I've been doing a lot with numba which allows you to add a simple wrapper to your code. For this one I just added #njit to a function incALot() which runs your code above.
On my machine CPython takes 61 seconds, but with the numba wrapper it takes 7.2 microseconds which will be similar to C and maybe faster than Go. Thats an 8 million times speedup.
So, in Python, if things with numbers seem a bit slow, there are tools to address it - and you still get Python's programmer productivity and the REPL.
def incALot(y):
x = 0
while x < y:
x += 1
#njit('i8(i8)')
def nbIncALot(y):
x = 0
while x < y:
x += 1
return x
size = 1000000000
start = time.time()
incALot(size)
t1 = time.time() - start
start = time.time()
x = nbIncALot(size)
t2 = time.time() - start
print('CPython3 takes %.3fs, Numba takes %.9fs' %(t1, t2))
print('Speedup is: %.1f' % (t1/t2))
print('Just Checking:', x)
CPython3 takes 58.958s, Numba takes 0.000007153s
Speedup is: 8242982.2
Just Checking: 1000000000

Problem is Python is interpreted, GO isn't so there's no real way to bench test speeds. Interpreted languages usually (not always have a vm component) that's where the problem lies, any test you run is being run in interpreted bounds not actual runtime bounds. Go is slightly slower than C in terms of speed and that is mostly due to it using garbage collection instead of manual memory management. That said GO compared to Python is fast because its a compiled language, the only thing lacking in GO is bug testing I stand corrected if I'm wrong.

It is possible that the compiler realized that you didn't use the "i" variable after the loop, so it optimized the final code by removing the loop.
Even if you used it afterwards, the compiler is probably smart enough to substitute the loop with
i = 1000000000;
Hope this helps =)

I'm not familiar with go, but I'd guess that go version ignores the loop since the body of the loop does nothing. On the other hand, in the python version, you are incrementing x in the body of the loop so it's probably actually executing the loop.

Optimizing python for loops

Here are two programs that naively calculate the number of prime numbers <= n.
One is in Python and the other is in Java.
public class prime{
public static void main(String args[]){
int n = Integer.parseInt(args[0]);
int nps = 0;
boolean isp;
for(int i = 1; i <= n; i++){
isp = true;
for(int k = 2; k < i; k++){
if( (i*1.0 / k) == (i/k) ) isp = false;
}
if(isp){nps++;}
}
System.out.println(nps);
}
}
`#!/usr/bin/python`
import sys
n = int(sys.argv[1])
nps = 0
for i in range(1,n+1):
isp = True
for k in range(2,i):
if( (i*1.0 / k) == (i/k) ): isp = False
if isp == True: nps = nps + 1
print nps
Running them on n=10000 I get the following timings.
shell:~$ time python prime.py 10000 && time java prime 10000
1230
real 0m49.833s
user 0m49.815s
sys 0m0.012s
1230
real 0m1.491s
user 0m1.468s
sys 0m0.016s
Am I using for loops in python in an incorrect manner here or is python actually just this much slower?
I'm not looking for an answer that is specifically crafted for calculating primes but rather I am wondering if python code is typically utilized in a smarter fashion.
The Java code was compiled with
javac 1.6.0_20
Run with java version "1.6.0_18"
OpenJDK Runtime Environment (IcedTea6 1.8.1) (6b18-1.8.1-0ubuntu1~9.10.1)
OpenJDK Client VM (build 16.0-b13, mixed mode, sharing)
Python is:
Python 2.6.4 (r264:75706, Dec 7 2009, 18:45:15)

As has been pointed out, straight Python really isn't made for this sort of thing. That the prime checking algorithm is naive is also not the point. However, with two simple things I was able to greatly reduce the time in Python while using the original algorithm.
First, put everything inside of a function, call it main() or something. This decreased the time on my machine in Python from 20.6 seconds to 14.54 seconds. Doing things globally is slower than doing them in a function.
Second, use Psyco, a JIT compiler. This requires adding two lines to the top of the file (and of course having psyco installed):
import psyco
psyco.full()
This brought the final time to 2.77 seconds.
One last note. I decided for kicks to use Cython on this and got the time down to 0.8533. However, knowing how to make the few changes to make it fast Cython code isn't something that I recommend for the casual user.

Yes, Python is slow, about a hundred times slower than C. You can use xrange instead of range for a small speedup, but other than that it's fine.
Ultimately what you're doing wrong is that you do this in plain Python, instead of using optimized libraries such as Numpy or Psyco.
Java comes with a jit compiler that makes a big difference where you're just crunching numbers.

You can make your Python about twice as fast by replacing that complicated test with
if i % k == 0: isp = False
You can also make it about eight times faster (for n=10000) than that by adding a break after that isp = False.
Also, do yourself a favor and skip the even numbers (adding one to nps to start to include 2).
Finally, you only need k to go up to sqrt(i).
Of course, if you make the same changes in the Java, it's still about 10x faster than the optimized Python.

Boy, when you said it was a naive implementation, you sure weren't joking!
But yes, a one to two order of magnitude difference in performance is not unexpected when comparing JIT-compiled, optimized machine code with an interpreted language. An alternative Python implementation such as Jython, which runs on the Java VM, may well be faster for this task; you could give it a whirl. Cython, which allows you to add static typing to Python and get C-like performance in some cases, may be worth investigating as well.
Even when considering the standard Python interpreter, CPython, though, the question is: is Python fast enough for the task at hand? Will the time you save writing the code in a dynamic language like Python make up for the extra time spent running it? If you had to write a given program in Java, would it seem like too much work to be worth the trouble?
Consider, for example, that a Python program running on a modern computer will be about as fast as a Java program running on a 10-year-old computer. The computer you had ten years ago was fast enough for many things, wasn't it?
Python does have a number of features that make it great for numerical work. These include an integer type that supports an unlimited number of digits, a decimal type with unlimited precision, and an optional library called NumPy specifically for calculations. Speed of execution, however, is not generally one of its major claims to fame. Where it excels is in getting the computer to do what you want with minimal cognitive friction.

If you're looking to do it fast, Python probably isn't the way forward, but you could speed it up a bit. First, you're using quite a slow way to test for divisibility. Modulo is quicker. You can also stop the inner loop (with k) as soon as it detects a match. I'd do something like this:
nps = 0
for i in range(1, n+1):
if all(i % k for k in range(2, i)): # i.e. if divisible by none of them
nps += 1
That brings it down from 25 s to 1.5 s for me. Using xrange brings it down to 0.9 s.
You could speed it up further by keeping a list of primes you've already found, and only testing those, rather than every number up to i (if i isn't divisible by 2, it won't be divisible by 4, 6, 8...).

Why don't you post something about the memory usage - and not just the speed? Trying to get a simple servlet on tomcat is wasting 3GB on my server.
What you did with the examples up there is not very good. You need to use numpy. Replace for/range with while loops, thus avoiding the list creation.
At last, python is quite suitable for number crunching, at least by people that do it the right way, and know what Sieve of Eratosthenes is, or mod operation is.

There are lots of things you can do to this algorithm to speed it up, but most of them would also speed up the Java version as well. Some of those will speed up the Python more than the Java, so they're worth testing.
Here's just a couple of changes that speed it up from 11.4 to 2.8 seconds on my system:
nps = 0
for i in range(1,n+1):
isp = True
for k in range(2,i):
isp = isp and (i % k != 0)
if isp: nps = nps + 1
print nps

Python is a language which, ironically, is well-suited for developing algorithms. Even a modified algorithm like this:
# See Thomas K for use of all(), many posters for sqrt optimization
nps = 0
for i in xrange(1, n+1):
if all(i % k for k in xrange(2, 1 + int(i ** 0.5))):
nps += 1
runs in significantly under one second. Code like this:
def eras(n):
last = n + 1
sieve = [0,0] + range(2, last)
sqn = int(round(n ** 0.5))
it = (i for i in xrange(2, sqn + 1) if sieve[i])
for i in it:
sieve[i*i:last:i] = [0] * (n//i - i + 1)
return filter(None, sieve)
is faster still. Or try out these.
The thing is, python is usually fast enough for designing your solution. If it is not fast enough for production, use numpy or Jython to goose more performance out of it. Or move it to a compiled language, taking your algorithm observations learned in python with you.

Yes, Python is one of the slowest practical languages you'll encounter. While loops are marginally faster than for i in xrange(), but ultimately Python will always be much, much slower than anything else.
Python has its place: Prototyping theory and ideas, or in any situation where the ability to produce code fast is more important than the code's performance.
Python is a scripting language. Not a programming language.