When You Know How Many Times a Loop Will Execute in Advance

for Loop

Compages

David Money Harris , Sarah L. Harris , in Digital Design and Computer Architecture (Second Edition), 2013

For Loops

for loops, like while loops, repeatedly execute a cake of lawmaking until a condition is not met. Notwithstanding, for loops add support for a loop variable, which typically keeps track of the number of loop executions. A general format of the for loop is

for (initialization; condition; loop operation)

statement

The initialization code executes before the for loop begins. The status is tested at the first of each loop. If the condition is non met, the loop exits. The loop operation executes at the end of each loop.

Code Case half dozen.twenty adds the numbers from 0 to 9. The loop variable, in this instance i, is initialized to 0 and is incremented at the end of each loop iteration. At the kickoff of each iteration, the for loop executes just when i is not equal to 10. Otherwise, the loop is finished. In this instance, the for loop executes 10 times. for loops can be implemented using a while loop, but the for loop is often user-friendly.

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Introduction to motorcar learning and Python

Hoss Belyadi , Alireza Haghighat , in Machine Learning Guide for Oil and Gas Using Python, 2021

For loop

For loop is another very useful tool in any programming language and allows for iterating through a sequence. Allow'southward define i to be a range between 0 and 5 (excluding 5). A for loop is and then written to result in writing 0 to 4. As shown below, " for x in i" is the same equally "for ten in range(0,five)"

i=range(0,5)

for 10 in i:

print(x)

Python output=

Another for loop example tin can be written every bit follows:

for ten in range(0,3):

impress('Edge calculating in the O&1000 manufacture is very valuable')

Python output=Edge computing in the O&Grand industry is very valuable

Edge calculating in the O&Grand industry is very valuable

Border calculating in the O&Thousand manufacture is very valuable

The "break" function allows stopping through the loop before looping through all the items. Below is an example of using an if statement and break part within the for loop. Every bit displayed below, if the for loop sees "Frac_Crew_2," information technology volition interruption and not finish the for-loop iteration.

Frac_Crews=['Frac_Crew_1', 'Frac_Crew_2', 'Frac_Crew_3', 'Frac_Crew_4']

for x in Frac_Crews:

print(x)

if x=='Frac_Crew_2':

break

Python output=Frac_Crew_1

Frac_Crew_2

With the "continue" statement, information technology is possible to cease the current iteration of the loop and go on with the next. For example, if it is desirable to skip "Frac_Crew_2" and move to the side by side proper noun, the go along statement can be used as follows:

Frac_Crews=['Frac_Crew_1', 'Frac_Crew_2', 'Frac_Crew_3', 'Frac_Crew_4']

for x in Frac_Crews:

if ten=='Frac_Crew_2':

continue

impress(x)

Python output=Frac_Crew_1

Frac_Crew_3

Frac_Crew_4

The "range" function can also exist used in different increments. By default, the range office uses the following sequence to generate the numbers: showtime, stop, increments. For example, if the number that is desirable to start with is x with the final number of 18, and an increment of 4, the following lines tin be written:

for x in range(ten, 19, 4):

impress(x)

Python output=10

fourteen

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Structured Design Methodologies

Konrad Morgan , in Encyclopedia of Information Systems, 2003

Iv.D.3.c. The "For" Loop

The For loop is rather different from the other examples nosotros have been looking at, considering when we use the For loop we know in advance exactly how many times nosotros want to execute the statements inside the loop. It is also different because it involves (and therefore introduces) the utilise of a counter or variable. This variable can best be described every bit the counter which the programme will use to count how many times the statements inside the loop volition be obeyed. Earlier the plan tin can perform a For loop it has to know some other items of information, as you would if I told you to repeat a set series of actions. The programmer and the program need to know how many times to perform the deportment. To give the program this information, the For loop insists that the programmer tell it how to use the variable it volition employ as a counter for the loop. The easiest way to exercise this would be to say to the program "obey this loop a number of times, using a specific variable, from the value of ane to a terminating value." This would exist one possible way of specifying a For loop, only we would lose a large corporeality of the usefulness (called functionality) of the For loop if we did specify it in this mode. For example, oftentimes we volition desire to commencement and cease the For loop counter at specific values, say counting from the initial value 6 to the terminating value 24, which would make the statements in the loop be repeated a total of 18 times. Every bit always, an example might be helpful, so imagine that we have a lecturer who has a habit of asking members of his course questions. However, he wants to exist sure that he never picks on whatever 1 fellow member of the grade more than any other. To do this he has given each member of the form a number, and he starts his questions with the student he associates with number one, and so ii, and so on until the whole class has been asked a question and has replied. We could evidence the logic involved in this by the following pseudo-code:

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Profiling and timing

Jim Jeffers , ... Avinash Sodani , in Intel Xeon Phi Processor High Performance Programming (Second Edition), 2016

TLB misses — tuning suggestions

For loops with multiple streams, it may be benign to split them into multiple loops to reduce TLB pressure (this may too help cache locality). When the addresses accessed in a loop differ past multiples of large powers of two, the effective size of the TLBs volition be reduced considering of associativity conflicts. Consider padding between arrays by one 4KB page.

If the L1 to L2 TLB miss ratio is high, and then consider using large pages.

In general, any program transformation that improves spatial locality volition do good both enshroud utilization and TLB utilization. The TLB is but another kind of cache.

If ITLB misses are a problem, so the PGO feature of the compiler is a expert pick to utilise to reduce the ITLB misses. The compiler divides the functions and basic blocks into hot and cold regions and generates a layout to improve the code locality which in plough makes the ITLB and ICACHE hit rate better.

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MATLAB Fundamentals

Brian D. Hahn , Daniel T. Valentine , in Essential MATLAB for Engineers and Scientists (Seventh Edition), 2019

2.7.3 Limit of a sequence

for loops are ideal for calculating successive members of a sequence (equally in Newton's method). The following instance also highlights a problem that sometimes occurs when computing a limit. Consider the sequence

$x_{due north} = \frac{a^{north}}{n!}, n = 1, 2, 3 \dots$

where a is any constant and north! is the factorial function defined above. The question is this: What is the limit of this sequence as due north gets indefinitely large? Let'due south take the case $a = 10$ . If nosotros endeavour to compute $x_{n}$ directly, we can go into problem, considering n! grows very rapidly every bit n increases, and numerical overflow can occur. Nevertheless, the state of affairs is neatly transformed if we spot that $x_{northward}$ is related to $x_{north - 1}$ as follows:

$x_{n} = \frac{a x_{northward - 1}}{due north} .$

There are no numerical bug now.

The post-obit program computes $x_{n}$ for $a = 10$ and increasing values of n.

a = x;

x = 1;

g = 20; % number of terms

for n = one:one thousand

ten = a * ten / north;

disp( [n x] )

end

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LabVIEW Graphical Programming Environment

Nasser Kehtarnavaz , in Digital Indicate Processing System Design (2nd Edition), 2008

2.4.4 Structures

A structure is represented by a graphical enclosure. The graphical code enclosed by a structure gets repeated or executed conditionally. A loop construction is equivalent to a For Loop or a While Loop statement encountered in text-based programming languages, whereas a Case structure is equivalent to an if-else argument.

ii.4.4.1 For Loop

A For Loop construction is used to perform repetitions. Every bit illustrated in Effigy 2-12, the displayed border indicates a For Loop structure, where the count terminal

represents the number of times the loop is to be repeated. Information technology is set by wiring a value from exterior the loop to it. The iteration terminal

denotes the number of completed iterations, which e'er starts at nil.

2.four.4.2 While Loop

A While Loop construction allows repetitions depending on a condition; see Figure ii-13. The conditional terminal

initiates a end if the status is true. Similar to a For Loop , the iteration terminal

provides the number of completed iterations, always starting at zero.

2.four.4.3 Case Structure

A Case structure, shown in Figure 2-14, allows running dissimilar sets of operations depending on the value it receives through its selector terminal, which is indicated past

. In addition to Boolean type, the input to a selector last tin can exist of integer, string, or enumerated type. This input determines which case to execute. The case selector

shows the status being executed. Cases can exist added or deleted as needed.

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Computational Statistics with R

Chaitra H. Nagaraja , in Handbook of Statistics, 2014

4.4 while Loops

All for loops can be written as while loops, the more than general type of loop. The general format of a while loop is: while(CONDITION){PROCESS}, where PROCESS is repeated until Condition is imitation. Nosotros rewrite our inner for loop in the previous section with a while loop to compute the average length of Old Faithful eruptions (from data true-blue):

> x <- faithful$eruptions

> # average the numbers in vector ten using a while() loop

> sum.x <- 0

> index <- ane # ready alphabetize

> while(index <= length(x)){ # loop repeated equally long as

> # index is less than or equal to length(ten)

> # compute cumulative sum

> sum.x <- sum.x + 10[alphabetize]

> if(alphabetize%%50==0) impress(alphabetize) # print index value (for debugging)

> # increase alphabetize

> index <- index+ane

> } # terminate while loop

> # compute average

> sum.x/length(x)

> # check code using built-in R role

> mean(10)

> # eruptions mean: 3.488

Prior to executing the lawmaking in PROCESS , the condition index <= length(x) is checked. The moment index, the counter, is greater than the number of observations in x, the loop ends. Annotation, over again, that nosotros use indentation to highlight the body of the loop. When the number of iterations may be large or each iteration is time consuming, print(index) which prints the number of completed iterations is helpful (in this case, we impress subsequently every 50 iterations). See the stop of Section 4.3 for some other example.

For a known number of iterations, a for loop is easier to write; notwithstanding, this number is non always known in advance. Optimization algorithms are an example of such a situation. Then, a while loop or a repeat loop with a break clause is necessary. As a second case, we write a while loop which finds the first 10 prime number numbers starting from 2:

> num.prime <- 0 # number of primes

> int <- two # showtime number to check if prime

> prime number.listing <- rep(NA, times=10) # empty vector to hold prime number

> # numbers as we find them

> while(num.prime < x){ # check whether we've found

> # the 10th prime

> num.divisor <- 0 # to count number of divisors > # for int

> for(i in 2:int){

> if(int%%i == 0) num.divisor <- num.divisor + i

> } # end for loop

> # if number of divisors is 1, int is prime number

> if(num.divisor == 1){

> num.prime <- num.prime + ane

> prime number.listing[num.prime] <- int

> } # end if statement

> int <- int+1 # need to examination next integer

> } # end while loop

> prime.list # print prime numbers

> # solution: two, iii, 5, 7, 11, 13, 17, 19, 23, 29

In this instance, the while loop repeats near 30 times to extract the outset 10 prime number numbers; we could non use a for loop to run this plan. It is possible (and like shooting fish in a barrel) to accidentally construct an infinite while loop, therefore, testing your code is key. The escape key is useful to finish such an infinite loop.

Above, we starting time constructed an empty vector, prime.list, then filled it within the while loop. This is more efficient than starting with a vector of length one and continuously appending to it as the loop progresses. Run into Section four.6 for more than details.

A last note: it is best to avert loops where possible equally they tend to employ more memory in R. For simple processes, vectorization can be used equally a substitute. Instead of looping through a vector or columns of a matrix, for instance, writing code that tin directly implement a function chemical element-wise is more than efficient. Examples of vectorized functions include nchar() or log(); in dissimilarity, sum() and det() are applied to unabridged objects. The functions employ(), lapply(), and amass(), which we describe in Section 6, are essential for vectorization also.

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Shared-Memory Programming with OpenMP

Peter S. Pacheco , in An Introduction to Parallel Programming, 2011

5.5.2 Data dependences

If a for loop fails to satisfy 1 of the rules outlined in the preceding section, the compiler will simply reject it. For instance, suppose we try to compile a plan with the following linear search function:

1: int Linear_search(int key, int A[], int n) {
ii: int i;
iii: /* thread_count is global */
4: # pragma omp parallel for num_threads(thread_count)
v: for (i = 0; i < n; i++)
six: if (A[i] == key) return i;
7: return -1; /* primal not in list */
viii: }

The gcc compiler reports:

Line 6: mistake: invalid leave from OpenMP structured cake

A more insidious trouble occurs in loops in which the computation in one iteration depends on the results of one or more previous iterations. As an example, consider the following code, which computes the starting time n fibonacci numbers:

fibo[0] = fibo[1] = 1;

for (i = 2; i < n; i++)

fibo[i] = fibo[i-1] + fibo[i-ii];

Although we may exist suspicious that something isn't quite right, allow'southward try parallellizing the for loop with a parallel for directive:

fibo[0] = fibo[1] = 1;

# pragma omp parallel for num_threads(thread_count)

for (i = ii; i < due north; i++)

fibo[i] = fibo[i-ane] + fibo[i-2];

The compiler will create an executable without complaint. However, if we try running it with more than than ane thread, we may observe that the results are, at best, unpredictable. For example, on one of our systems if we try using 2 threads to compute the first x Fibonacci numbers, nosotros sometimes become

i 1 2 3 v 8 13 21 34 55,

which is correct. However, we too occasionally go

one ane ii three 5 8 0 0 0 0.

What happened? Information technology appears that the run-fourth dimension system assigned the computation of fibo[2], fibo[3], fibo[4], and fibo[v] to 1 thread, while fibo[half-dozen], fibo[7], fibo[8], and fibo[ix] were assigned to the other. (Remember the loop starts with i = 2.) In some runs of the program, everything is fine because the thread that was assigned fibo[2], fibo[iii], fibo[four], and fibo[v] finishes its computations earlier the other thread starts. However, in other runs, the showtime thread has plain not computed fibo[4] and fibo[five] when the second computes fibo[vi]. It appears that the arrangement has initialized the entries in fibo to 0, and the second thread is using the values fibo[4] = 0 and fibo[five] = 0 to compute fibo[six]. Information technology then goes on to employ fibo[5] = 0 and fibo[6] = 0 to compute fibo[seven], and then on.

We meet ii important points here:

1.: OpenMP compilers don't check for dependences amid iterations in a loop that'south beingness parallelized with a parallel for directive. It's up to us, the programmers, to identify these dependences.
2.: A loop in which the results of i or more than iterations depend on other iterations cannot, in general, be correctly parallelized by OpenMP.

The dependence of the ciphering of fibo[6] on the ciphering of fibo[5] is chosen a data dependence. Since the value of fibo[five] is calculated in one iteration, and the result is used in a subsequent iteration, the dependence is sometimes called a loop-carried dependence.

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Annals-Level Communication in Speculative Bit Multiprocessors

Milan B. Radulović , ... Veljko M. Milutinović , in Advances in Computers, 2014

5.2.iii Description of ESIC Protocol

The annals advice between threads in ESIC tin be producer-initiated and consumer-initiated as in SIC protocol. The processor-induced and motorbus-induced state transitions for loop-live registers in ESIC protocol are presented in Fig. 1.23.

The protocol for loop-live registers works equally follows:

one.

Read hit. As in the SIC, the read request for a register in a valid state (here also including VPS and VPSF) is satisfied locally without state change.

ii.

Read miss. A read request for a register in the INV land initiates BusR transaction, as in the SIC. The procedure is the same and simply difference is that possible supplier can be also a predecessor thread with requested register in the VPS state. If the requested annals was in the VPS state at the supplier's side, it goes from VPS to VPSF state to go along track of speculative forwarding. The requested register is loaded in the VU state at the consumer's side.

In the SIC protocol, a consumer thread is blocked if there is no supplier of a safe value available when the consumer's read request is issued on the jitney. However, in the same situation, the ESIC protocol prevents blocking if there is a supplier of a possibly safe value. A consumer thread in the ESIC blocks only if there is no supplier of neither a safe nor a possibly safe value.

3.

Write hit. The reaction in cases of the NFW request to a annals value in the VU state and of the FW request to a annals value in the VU or VPS state is the same as in the SIC. However, if a FW request finds a register value in the VPSF state, the thread first sends the Squash indicate on the bus to the successor thread(s) that loaded a given register value earlier to undo the furnishings of incorrect speculation. Later that, it repeats the aforementioned procedure as in the case of the FW request to the VPS land to provide a rubber value to the successors.

In example of the PFW request to a annals value in the VU state, the register is updated locally and its state is inverse from the VU to VPS. If the PFW request finds a register value in the VPS state, the register is updated locally and it remains in the VPS land. Finally, when the PFW finds a register value in the VPSF country, the thread issues the Squash signal on the bus for successor thread(s) that loaded a given register value earlier. And so, the register is updated locally and goes from VPSF to VPS.

4.

Write miss. A thread's NFW to a register value in the INV state simply updates the register value fugitive the BusW transaction. The annals land is modified from the INV to VU country. The aforementioned reaction occurs when a thread performs the PFW to a register value in the INV country but the destination state is the VPS. The FW request to a register in the INV state updates the register value and changes its state from the INV to VS state. Likewise, it incurs a producer-initiated interthread communication in class of a BusW transaction. The producer thread puts the annals value on the double-decker and if the successor thread loaded the sent value, it raises Shared signal and sets the country to the VU. The destination state in the producer thread is either VS or LC depending on observed Shared signal.

The protocol for not-loop-alive registers works in the same manner as in the SIC.

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The CUDA Execution Model

In CUDA Application Pattern and Development, 2011

ILP: Higher Performance at Lower Occupancy

Loftier occupancy does non necessarily interpret into the fastest application functioning. Instruction-level parallelism (ILP) can be equally effective in hiding arithmetic latency by keeping the SIMD cores busy with fewer threads that consume fewer resources and introduce less overhead.

The reasoning for ILP is simple and powerful: using fewer threads ways that more registers can be used per thread. Registers are a precious resource, every bit they are the only memory fast enough to attain peak GPU performance. The larger the bandwidth gap between the annals shop and other memory, the more data that must come up from registers to attain high performance.

Sometimes having a few more than registers per thread can prevent register spilling and preserve high performance. Although necessary, register spilling violates the developer'due south expectation of loftier operation from register retentiveness and can crusade catastrophic performance decreases. Utilizing fewer threads also benefit kernels that use shared retentiveness by reducing the number of shared retentivity accesses and past allowing data reuse inside a thread (Volkov, 2010). A small-scale benefit includes a reduction in some of the work that the GPU must perform per thread.

The following loop, for example, would consume 2048 bytes of register storage and require that the loop counter, i, be incremented 512 times in a block with 512 threads. A thread block containing only 64 threads would require only 256 bytes of annals storage and reduce the number of integer increment in place operations by a gene of 4. Encounter Example 4.4 , "Simple for Loop to Demonstrate ILP Benefits":

Instance 4.4

for(int i=0; i < n; i++) …

Reading horizontally across the columns, Table 4.2 encapsulates how the number of registers per threads increases as the occupancy decreases for diverse compute generations.

Table 4.2. Increasing Registers Per Thread as Occupancy Decreases

	Maximum Occupancy	Maximum Registers	Increase
GF100	20 at 100% occupancy	63 at 33% occupancy	3x more than registers per thread
GF200	16 at 100% occupancy	≈128 at 12.v% occupancy	8x more registers per thread

ILP Hides Arithmetic Latency

Equally with TLP, multiple threads provide the needed parallelism. For instance, the shaded row in Figure four.iii highlights four contained operations that happen in parallel across iii threads.

Tabular array 4.3. A Set of TLP Arithmetic Operations

Thread 1	Thread 2	Thread iii	Thread 4
ten = x + c	y = y + c	z = z + c	w = w + c
x = ten + b	y = y + b	z = z + b	due west = w + b
x = x + a	y = y + a	z = z + a	w = w + a

Due to warp scheduling, parallelism tin also happen among the instructions within a thread, as long as there are enough threads to create two or more warps inside a block.

Table 4.four. Education Rearranged for ILP

	Thread
Instructions ->	westward = w + b	Four independent operations
	z = z + b
	y = y + b
	ten = x + b
	w = w + a	Four independent operations
	z = z + a
	y = y + a
	x = x + a

The following example demonstrates ILP by creating 2 or more than warps that run on a unmarried SM. Every bit can be seen in Case 4.5, "Arithmetic ILP Criterion," the execution configuration specifies merely i block. The number of warps resident on the SM is increased every bit the number of threads within the block is increased from 32 to 1024, and the performance is reported. This example volition run to completion on a compute 2.0 device that can support 1024 threads per cake. Earlier devices will notice a runtime mistake via the call to cudaGetLastError, which will stop the test when the maximum number of threads per block exceeds the number that the GPU tin can support. Because kernel launches are asynchronous, cudaSynchronizeThread is used to look for kernel completion.

Case four.5

#include <omp.h>

#include <iostream>

using namespace std;

#include <cmath>

//create storage on the device in gmem

__device__ float d_a[32], d_d[32];

__device__ float d_e[32], d_f[32];

#define NUM_ITERATIONS ( 1024 * 1024)

#ifdef ILP4

// test instruction level parallelism

#ascertain OP_COUNT 4*2*NUM_ITERATIONS

__global__ void kernel(bladder a, float b, float c)

{

#pragma unroll 16

for(int i=0; i < NUM_ITERATIONS; i++) {

a = a * b + c;

d = d * b + c;

due east = east * b + c;

f = f * b + c;

}

// write to gmem so the work is not optimized out by the compiler

d_a[threadIdx.x] = a; d_d[threadIdx.x] = d;

d_e[threadIdx.10] = e; d_f[threadIdx.x] = f;

}

#else

// examination thread level parallelism

#define OP_COUNT 1*2*NUM_ITERATIONS

__global__ void kernel(bladder a, bladder b, float c)

{

#pragma unroll 16

for(int i=0; i < NUM_ITERATIONS; i++) {

a = a * b + c;

}

// write to gmem so the work is not optimized out by the compiler

d_a[threadIdx.ten] = a;

}

When You Know How Many Times a Loop Will Execute in Advance

for Loop

Compages

For Loops

Introduction to motorcar learning and Python

For loop

Structured Design Methodologies

Iv.D.3.c. The "For" Loop

Profiling and timing

TLB misses — tuning suggestions

MATLAB Fundamentals

2.7.3 Limit of a sequence

LabVIEW Graphical Programming Environment

2.4.4 Structures

ii.4.4.1 For Loop

2.four.4.2 While Loop

2.four.4.3 Case Structure

Computational Statistics with R

4.4 while Loops

Shared-Memory Programming with OpenMP

5.5.2 Data dependences

Annals-Level Communication in Speculative Bit Multiprocessors

5.2.iii Description of ESIC Protocol

The CUDA Execution Model

ILP: Higher Performance at Lower Occupancy

ILP Hides Arithmetic Latency

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When You Know How Many Times a Loop Will Execute in Advance

for Loop

Compages

For Loops

Introduction to motorcar learning and Python

For loop

Structured Design Methodologies

Iv.D.3.c. The "For" Loop

Profiling and timing

TLB misses — tuning suggestions

MATLAB Fundamentals

2.7.3 Limit of a sequence

LabVIEW Graphical Programming Environment

2.4.4 Structures

ii.4.4.1 For Loop

2.four.4.2 While Loop

2.four.4.3 Case Structure

Computational Statistics with R

4.4 while Loops

Shared-Memory Programming with OpenMP

5.5.2 Data dependences

Annals-Level Communication in Speculative Bit Multiprocessors

5.2.iii Description of ESIC Protocol

The CUDA Execution Model

ILP: Higher Performance at Lower Occupancy

ILP Hides Arithmetic Latency

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