Data dependence analysis of assembly code springerlink. Data dependence is true dependence the consumer instruction cant be scheduled before the producer one readafterwrite dependences may be caused by the name of the storage used in the instructions, not by the producerconsumer relationship these are false dependences anti dependence writeafterread. Identify the presence of crossiteration data dependences. The basic idea of prospector is dividing the work between software tools and programmers to maximize the overall performance bene.
Instructionlevel parallelism ilp finegrained parallelism obtained by. In compiler theory, the technique used to discover data dependencies among statements or instructions is called dependence analysis. Data dependence true, anti, output dependence source and sink distance vector, direction vector relation between reordering transformation and direction. Note that under the dependenceaware parallelization shown, the parallel execution is equivalent to sequentially processing minibatches d 1, d 4, d 2, and d 3 serializable, while under the shown dataparallelism, execution is not serializable. However, in more complex cases involving triangular or trapezoidal loop regions, symbolic. According to the new order of loops, exchange the elements in the direction vectorsto derive the new direction vectors. Data dependence testing is required to detect parallelism in programs. Prospector aims to bridge the gap between automatic and manual parallelization.
In order to discuss data dependencies it is important to first discuss the. If we can determine that no data dependencies exist between the di erent iterations of a loop we may be able to run the loop in parallel or transform it to make better use of the cach e. Instruction vs machine parallelism instructionlevel parallelism ilp of a programa measure of the average number of instructions in a program that, in theory, a processor might be able to execute at the same time mostly determined by the number of true data. Data dependence analysis for automatic parallelization of sequential tree codes is discussed. Possibility of a hazard order in which results must be. Instr j tries to read operand before instr i writes it 2. Common types of dependencies include data dependence, name dependence. Dependence based code transformation for coarsegrained parallelism.
Data dependence allow compilers to model the relation between the operations on data points and check validity of operations that can be performed concurrently. Very little work has been done in the field of data dependence analysis on assembly language code, but this area will be of growing importance, e. Pipeline organization determines if dependence is detected and if it causes a stall data dependence conveys. Distributed memory communicate required data at synchronization points. Data dependences and parallelization stanford infolab. However, for sparse matrix computations, parallelization based on solely exploiting the existing parallelism in an algorithm does not always give satisfactory. Transformations that involve both control and data dependence cannot be specified in a consistent manner with this form, however, since control is. Data dependence computation in parallel and vector constructs as well as serial do loops. The data dependence or true dependence refers to the case when a variable content updated by an instruction is used by another instruction following it readafterwrite case. Use a set of heuristics to examine the inequalities loop residue graph and etcif still not sure, continue. Here we assume that a legal and desirable orderingis given. A dependence occurs when more than one task is using a variable in a program.
Data dependence analysis data dependence analysis i data dependence analysis determines what the constraints a re on how a piece of code can be reorganized. You might, for example, have each cpu core calculate one frame of data where there are no interdependencies between frames. Pdf effectiveness of data dependence analysis researchgate. Gcd test use diophantine equationif failed, no data dependences, otherwise, continue. Publications software analytics and pervasive parallelism lab. The data dependence profiler serves as the foundation of the parallelism discovery framework. Data dependence computation in parallel and vector constructs as well as serialdo loops is covered. The aforementioned two approaches are based on exploiting the existing parallelism i. The program dependence graph and its use in optimization. Based on the above discussion, the primary data dependence during sparse lu factorization is the columnlevel data dependence. The data flow graph 36,37 represents global data dependence at the operator level called the atomic level in 31. Bo zhao, zhen li, ali jannesari, felix wolf, weiguo wu.
Class notes 18 june 2014 detecting and enhancing loop. The program flow graph displays the patterns of simultaneously executable. Solving ilp for data dependence does data dependence exist in a loop. With interlocks, data dependence causes a hazard and stall without interlocks, data dependence prohibits the compiler from scheduling instructions with overlap data dependence conveys. It should be stressed that our proposed technique is generic to all data dependent parallel patterns and in no way limited to the examples presented here. Traditional analysis is inadequate for parallelization. Only needs to fhfetch one instruction per data operation. Traditional dependence profiling approaches introduce a tremendous amount of. Data dependence specialization highthroughput data processors face similar problems. The tg can also be seen as a data dependence graph ddg at the task level. If all the direction vectors havea data dependent parallel pattern 7, 21, 35, as a running illustrative example due to its simple structure and clarity in conveying concepts in this paper. A data dependence results from multiple uses of the same locations in storage by different tasks.
Tasklevel parallelism an overview sciencedirect topics. Difficulties in data dependence analysis usually analysis is more difficult because of more complex data types determining if a reference is to the same data as another access is the problem of determining aliasing one access aliases another access, if the accesses overlap data in memory. Thread block a thread block is a group of threads which execute on the same multiprocessor smx. The data dependence graph is a powerful means for designing and analyzing parallel algorithms. Dependencies are one of the primary inhibitors to parallelism. Data parallelism simple english wikipedia, the free. There is a high degree of parallelism to be exploited in the center of the matrix but the degree parallelism is low towards the two corners. I raw read after write i waw write after write i war write after read rar read after readis not a hazard.
A survey of data dependence analysis techniques for automated. They reference the same array cell one of them is a write the two associated statements are executed two memory accesses and are data dependent if. For some classes of programs, static analysis and automatic parallelization is feasible 5, but with the current stateoftheart, most soft ware requires manual. Section 4 gives examples of loop transformations using data dependence. Static data dependence let a and a be two static array accesses not necessarily distinct data dependence exists from a to a, iff either a or a is a write operation there exists a dynamic instance of a o and a dynamic instance of a o such that o and o may refer to the same location o executes before o. An efficient datadependence profiler for sequential and parallel. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups. Data dependence testing is the basic step in detecting loop level parallelism in numerical programs.
Static data dependence let a and a be two static array accesses not necessarily distinct data dependence exists from a to a, iff either a or a is a write operation there exists a dynamic instance of a o and a dynamic instance of a o such that o and o may. Possibility of a hazard negative sideeffect if not in order the required order of instructions upper bound on achievable parallelism. Compiler writers and computer architects have investigated the use of value speculation for extracting instructionlevel parallelism. Programs, which are data intensive, like video encoding, for example, use the data parallelism model and split the task in n parts where n is the number of cpu cores available. They reference the same array cell one of them is a write the two associated statements are executed two memory accesses and are data. Automating dependenceaware parallelization of machine. Function level parallelism driven by data dependencies core. Graph transformation and designing parallel sparse matrix. Instruction vs machine parallelism instructionlevel parallelism ilp of a programa measure of the average number of instructions in a program that, in theory, a processor might be able to execute at the same time mostly determined by the number of true data dependencies and procedural control dependencies in. The degree of parallelism is revealed in the program profile or in the program flow graph.
Pdf data dependence testing is the basic step in detecting loop level parallelism in numerical programs. Complexity refers to the number of indices appearing within a. Data dependence analysis techniques for increased accuracy. Software parallelism is a function of algorithm, programming style, and compiler optimization.
In this brief, a columnlevel parallelism is exploited. Most real programs fall somewhere on a continuum between task parallelism and data parallelism. Dependencies, instruction scheduling, optimization, and. Prospector divides the work between software tools and programmers to. The process of parallelizing a sequential program can be broken down into four discrete steps. Data hazards a hazard exists whenever there is a name or data dependence between two instructions and they are close enough that their overlapped execution would violate the programs order of dependency. Figure 3 shows the dependence structure of the two. They reference the same array cell one of them is a write the two associated statements are executed two memory accesses and are data dependent iff. In lexical analysis, the source of the dependence is the fsm state. Fusion can enhance locality by reducing the time between uses of the same data, thereby increasing the likelihood of the data being retained in the cache.
A data dependency in computer science is a situation in which a program statement instruction refers to the data of a preceding statement. It is defined by the control and data dependence of programs. Kim, chikeung ck luk, hyesoon kim college of computing, georgia institute of technology, atlanta, ga intel corporation, hudson, ma abstractmultiprocessor architectures are increasingly common these days. Array dependence analysis enables optimization for parallelism in programs involving arrays. Teaching parallel computing and dependence analysis with. Data dependence and its application to parallel processing. Data dependence profiling for parallel programming citeseerx. Data dependence definition given two memory references, there exists a dependence between them if the three following conditions hold. An efficient datadependence profiler for sequential and parallel programs. Determination of data dependences is a task typically performed with highlevel language source code in todays optimizing and parallelizing compilers. A dynamic datadependence profiler to help parallel. Program dependence graph and its use in optimization 321 2. Instruction j is data dependent on instruction k and instruction k is data dependent on instruction i dependent instructions cannot be executed simultaneously pipeline organization determines if dependence is detected and if it causes a stall data dependence conveys. Data parallelism emphasizes the distributed parallel nature of the data, as opposed to the processing task parallelism.
Traditional data dependence analysis techniques, such as the banerjee test and the itest, can efficiently compute data dependence information for simple instances of the data dependence problem. Encapsulate data in a sycl application across both devices and host. Applying this framework to sequential programs can teach us how much parallelism is present in a program, but also tells us what the most appro priate parallel. Note that under the dependence aware parallelization shown, the parallel execution is equivalent to sequentially processing minibatches d 1, d 4, d 2, and d 3 serializable, while under the shown data parallelism, execution is not serializable.
Related work much related work has been performed over the past ten years in the area of dependencebased program representations. Cs 293s parallelism and dependence theory ucsb computer. Dependencies are important in parallel programming because they are the main inhibitor to parallelism. Pdf dependence driven execution for data parallelism. Our current aim is to provide a convenient programming environment for smp parallelism, and especially multicore architectures. Pdf function level parallelism driven by data dependencies. Parallelizing compilers rely on data dependence information in order to produce valide parallel code. List the direction vectors of all types of data dependencesin the original program 2. Data parallelism also known as looplevel parallelism is a form of parallel computing for multiple processors using a technique for distributing the data across different parallel processor nodes.
Instruction level parallelism 1 compiler techniques. Data parallelism in gpus gpus take advantage of massive dlp to provide very high flop rates more than 1 tera dp flop in nvidia gk110 simt execution model single instruction multiple threads trying to distinguish itself from both vectors and simd. Dennis work 18 opened up the area of data flow computation 19. A survey of data dependence analysis techniques for.
Data dependence analysis for the parallelization of. One critical part of exposing parallelism in the loop nests is the analysis of data dependence 14, 3. Threads in a grid execute a kernel function and are divided into thread blocks. On top of this, supporting data dependence makes computing or accessing arbitrary datatypes 8bit,16bit,32bit more difficult. It contrasts to task parallelism as another form of parallelism in a multiprocessor system where each one is executing a single set of instructions, data parallelism is achieved when each. Value speculation is a mechanism for increasing parallelism by predicting values of data dependencies between tasks. This is a data dependence in order to analyze looplevel parallelism, we need to determine whether there is a loopcarried dependence i.
The simultaneous execution of multiple instructions from a program. Hierarchical numerical algorithms of ten use tree data. Finding parallelism that exists in a software program depends a great deal on determining. Keywordsdata dependence, profiling, program analysis, par allelization, parallel programming. Towards general purpose acceleration by exploiting common. Instructionlevel parallelism an overview sciencedirect. Data dependence true, anti, output dependence source and sink distance vector, direction vector relation between reordering transformation and direction vector loopdependence loopcarried dependence loopindependent dependences. Prospector provides candidates of parallelizable loops to programmers which were discovered by dynamic pro. For instance, it does not distinguish between different executions of the same statement in a loop. Pacheco, in an introduction to parallel programming, 2011. Shared memory synchronize readwrite operations between tasks. J is data dependent aka true dependence on instr i. Guzzi, cedar fortran programmers manual, document no. The name dependence could be either an anti dependence, or an output.
Possibility of a hazard order in which results must be calculated upper bound on exploitable instruction level parallelism dependencies that flow through memory locations are difficult to. Identify the presence of crossiteration datadependences traditional analysis is inadequate for parallelization. Compiler optimisation 8 dependence analysis school of. Discovering parallelism via dynamic datadependence pro. Common types of dependencies include data dependence, name dependence, and control dependence. A tg represents the application as a collection of tasks along with the control and data dependences between them, and thus can be used to identify tasklevel parallelism opportunities, including tasklevel pipelining. Cuda dynamic parallelism programming guide 2 glossary definitions for terms used in this guide. Dependence graph nodes for statements edges for data dependences labels on edges for dependence levels and types s1 s2. While pipelining is a form of ilp, the general application of ilp goes much further into more aggressive techniques to achieve parallel execution of the instructions in the instruction stream. The traditional approach of subword simd used by eg. Teaching parallel computing and dependence analysis with python.
1266 152 916 84 1075 1037 1423 1415 1339 987 463 1262 1537 868 1351 1524 1298 1553 705 615 771 56 946 303 61 63 1326 431 1181 1159 845