Algorithmic Differentiation of Pragma-Defined Parallel by Michael Förster

By Michael Förster

Numerical courses usually use parallel programming recommendations reminiscent of OpenMP to compute the program's output values as effective as attainable. moreover, by-product values of those output values with recognize to sure enter values play an important position. to accomplish code that computes not just the output values at the same time but additionally the spinoff values, this paintings introduces a number of source-to-source transformation principles. those ideas are in line with a strategy referred to as algorithmic differentiation. the main target of this paintings lies at the very important opposite mode of algorithmic differentiation. The inherent data-flow reversal of the opposite mode needs to be dealt with correctly through the transformation. the 1st a part of the paintings examines the modifications in a truly normal manner on the grounds that pragma-based parallel areas happen in lots of other kinds corresponding to OpenMP, OpenACC, and Intel Phi. the second one half describes the transformation ideas of an important OpenMP constructs.

Show description

Read Online or Download Algorithmic Differentiation of Pragma-Defined Parallel Regions: Differentiating Computer Programs Containing OpenMP PDF

Best machine theory books

Control of Flexible-link Manipulators Using Neural Networks

Keep an eye on of Flexible-link Manipulators utilizing Neural Networks addresses the problems that come up in controlling the end-point of a manipulator that has an important quantity of structural flexibility in its hyperlinks. The non-minimum part attribute, coupling results, nonlinearities, parameter adaptations and unmodeled dynamics in this type of manipulator all give a contribution to those problems.

Fouriertransformation für Ingenieur- und Naturwissenschaften

Dieses Lehrbuch wendet sich an Studenten der Ingenieurfächer und der Naturwissenschaften. Durch seinen systematischen und didaktischen Aufbau vermeidet es ungenaue Formulierungen und legt so die Grundlage für das Verständnis auch neuerer Methoden. Indem die klassische und die Funktionalanalysis auf der foundation des Fourieroperators zusammengeführt werden, vermittelt es ein fundiertes und verantwortbares Umgehen mit der Fouriertransformation.

Automated Theorem Proving: Theory and Practice

Because the twenty first century starts, the ability of our magical new device and companion, the pc, is expanding at an magnificent cost. desktops that practice billions of operations consistent with moment are actually standard. Multiprocessors with hundreds of thousands of little desktops - quite little! -can now perform parallel computations and resolve difficulties in seconds that very few years in the past took days or months.

Practical Probabilistic Programming

Sensible Probabilistic Programming introduces the operating programmer to probabilistic programming. during this e-book, you will instantly paintings on sensible examples like construction a junk mail clear out, diagnosing laptop method facts difficulties, and improving electronic photos. you will discover probabilistic inference, the place algorithms assist in making prolonged predictions approximately matters like social media utilization.

Extra info for Algorithmic Differentiation of Pragma-Defined Parallel Regions: Differentiating Computer Programs Containing OpenMP

Example text

3: The execution of a parallel region with p threads. the addresses of x and y on the screen through a function call to fprintf. After the parallel region the master thread prints its memory addresses of x and y through another call to fprintf. f p r i n t f ( s t d e r r , " I n s i d e / O u t s i d e ␣ o f ␣ ␣ ␣ ␣ ␣ \n" ) ; f p r i n t f ( stderr , " p a r a l l e l ␣ r e g i o n ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣&x ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣ ␣&y \n" ) ; f p r i n t f ( stderr , "−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−\n" ); float x; float y; omp_set_num_threads ( 2 ) ; #pragma omp p a r a l l e l { float x; f p r i n t f ( stderr , " I n s i d e ␣ ( t h r e a d ␣%d ) ␣ ␣%15p␣ ␣%15p\n" , omp_get_thread_num ( ) , &x , &y ) ; 34 1 Motivation and Introduction } f p r i n t f ( stderr , " O u t s i d e ␣ ( t h r e a d ␣%d ) ␣%15p␣ ␣%15p\n" , omp_get_thread_num ( ) , &x , &y ) ; The output looks as follows: I n s i d e / Outside of pa ra ll el region &x &y −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−− I n s i d e ( thread 1) 0 x2b6c3d9ffe2c 0 x7ffff35a6238 I n s i d e ( thread 0) 0 x7ffff35a620c 0 x7ffff35a6238 Outside ( thread 0) 0 x 7 f f f f 3 5 a 6 2 3 c 0 x7ffff35a6238 We have two lines showing the addresses from inside the parallel region, one is for the thread with the ID zero, the other one is for thread one.

Obviously, this requires the usage of additional thread local memory such that the different computations do not interfere with each other. The synchronization is prevented but paid with the price of redundant computations and an higher memory usage. Another extension, recommended by [11], is to use preprocessed loop bounding scheduling. This is possible here as all loops corresponding to a gradient computation have the exact same number of iterations. In case that the original code Q already contains OpenMP directives, the above approach can still be applied by using nested parallelism.

1 #pragma omp p a r a l l e l 2 { 3 ... 4 w h i l e ( i ≤ ub ) { 5 j←0; 6 y← 0 . 1 9 10 11 12 13 14 15 16 } 17 } 45 j ← j +1; } #pragma omp c r i t i c a l { t h r e a d _ r e s u l t 0← s i n ( t h r e a d _ r e s u l t 0 ) ∗ s i n ( y ) ; } i ← i +1; The data decomposition is here only indicated by dots. A thread local result is computed by using the private variable y. Afterwards, all results from the team of threads flow into a computation where the reference thread_result[0] is successively updated by the assignment inside the critical construct.

Download PDF sample

Rated 4.04 of 5 – based on 26 votes