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. Graph transformation and designing parallel sparse matrix. Identify the presence of crossiteration data dependences. Related work much related work has been performed over the past ten years in the area of dependencebased program representations. 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. 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. Tasklevel parallelism an overview sciencedirect topics. Compiler optimisation 8 dependence analysis school of. Here we assume that a legal and desirable orderingis given. Finding parallelism that exists in a software program depends a great deal on determining. Data dependence analysis of assembly code springerlink. Instr j tries to read operand before instr i writes it 2. Common types of dependencies include data dependence, name dependence, and control dependence. 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.
The data flow graph 36,37 represents global data dependence at the operator level called the atomic level in 31. Data dependence testing is the basic step in detecting loop level parallelism in numerical programs. One critical part of exposing parallelism in the loop nests is the analysis of data dependence 14, 3. Pacheco, in an introduction to parallel programming, 2011. 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. 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. 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. 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. In this brief, a columnlevel parallelism is exploited. In lexical analysis, the source of the dependence is the fsm state. The name dependence could be either an anti dependence, or an output. Cs 293s parallelism and dependence theory ucsb computer. Towards general purpose acceleration by exploiting common.
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. I raw read after write i waw write after write i war write after read rar read after readis not a hazard. Traditional analysis is inadequate for parallelization. Instruction level parallelism 1 compiler techniques. Use a set of heuristics to examine the inequalities loop residue graph and etcif still not sure, continue. Dependence graph nodes for statements edges for data dependences labels on edges for dependence levels and types s1 s2.
Guzzi, cedar fortran programmers manual, document no. 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. The data dependence profiler serves as the foundation of the parallelism discovery framework. Data dependence analysis for the parallelization of. This is a data dependence in order to analyze looplevel parallelism, we need to determine whether there is a loopcarried dependence i. Data dependence testing is required to detect parallelism in programs. 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.
A data dependency in computer science is a situation in which a program statement instruction refers to the data of a preceding statement. Prospector divides the work between software tools and programmers to. However, for sparse matrix computations, parallelization based on solely exploiting the existing parallelism in an algorithm does not always give satisfactory. A dynamic datadependence profiler to help parallel. Program dependence graph and its use in optimization 321 2. Dependencies, instruction scheduling, optimization, and.
Pdf data dependence testing is the basic step in detecting loop level parallelism in numerical programs. 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. 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. The simultaneous execution of multiple instructions from a program. 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. Data dependence analysis techniques for increased accuracy. For instance, it does not distinguish between different executions of the same statement in a loop. Data dependence specialization highthroughput data processors face similar problems.
Class notes 18 june 2014 detecting and enhancing looplevel. The aforementioned two approaches are based on exploiting the existing parallelism i. On top of this, supporting data dependence makes computing or accessing arbitrary datatypes 8bit,16bit,32bit more difficult. 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. 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.
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. The program flow graph displays the patterns of simultaneously executable. List the direction vectors of all types of data dependencesin the original program 2. 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. An efficient datadependence profiler for sequential and parallel programs. Compiler writers and computer architects have investigated the use of value speculation for extracting instructionlevel parallelism. The degree of parallelism is revealed in the program profile or in the program flow graph. The program dependence graph and its use in optimization.
J is data dependent aka true dependence on instr i. You might, for example, have each cpu core calculate one frame of data where there are no interdependencies between frames. A survey of data dependence analysis techniques for. The traditional approach of subword simd used by eg. Data dependence profiling for parallel programming citeseerx. Data dependence and its application to parallel processing. Cuda dynamic parallelism programming guide 2 glossary definitions for terms used in this guide. 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. Class notes 18 june 2014 detecting and enhancing loop. Value speculation is a mechanism for increasing parallelism by predicting values of data dependencies between tasks. Parallelizing compilers rely on data dependence information in order to produce valide parallel code. Pdf dependence driven execution for data parallelism. Our current aim is to provide a convenient programming environment for smp parallelism, and especially multicore architectures. The process of parallelizing a sequential program can be broken down into four discrete steps.
Determination of data dependences is a task typically performed with highlevel language source code in todays optimizing and parallelizing compilers. Dependence based code transformation for coarsegrained parallelism. Encapsulate data in a sycl application across both devices and host. A survey of data dependence analysis techniques for automated. Figure 3 shows the dependence structure of the two. Thread block a thread block is a group of threads which execute on the same multiprocessor smx. Instructionlevel parallelism and its exploitation part i. Common types of dependencies include data dependence, name dependence. In order to discuss data dependencies it is important to first discuss the. Only needs to fhfetch one instruction per data operation. A dependence occurs when more than one task is using a variable in a program.
Shared memory synchronize readwrite operations between tasks. Prospector aims to bridge the gap between automatic and manual parallelization. It is defined by the control and data dependence of programs. Teaching parallel computing and dependence analysis with python.
Data dependence allow compilers to model the relation between the operations on data points and check validity of operations that can be performed concurrently. 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. Most real programs fall somewhere on a continuum between task parallelism and data parallelism. Traditional dependence profiling approaches introduce a tremendous amount of time and memory overhead. Dennis work 18 opened up the area of data flow computation 19.
The tg can also be seen as a data dependence graph ddg at the task level. 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 dependent iff. Instructionlevel parallelism ilp finegrained parallelism obtained by. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups. Pipeline organization determines if dependence is detected and if it causes a stall data dependence conveys.
Based on the above discussion, the primary data dependence during sparse lu factorization is the columnlevel data dependence. In compiler theory, the technique used to discover data dependencies among statements or instructions is called dependence analysis. Complexity refers to the number of indices appearing within a. Data dependences and parallelization stanford infolab. Dependencies are important in parallel programming because they are the main inhibitor to parallelism. Hierarchical numerical algorithms of ten use tree data. Threads in a grid execute a kernel function and are divided into thread blocks. Prospector provides candidates of parallelizable loops to programmers which were discovered by dynamic pro. Data parallelism emphasizes the distributed parallel nature of the data, as opposed to the processing task parallelism. Discovering parallelism via dynamic datadependence pro. Several transformations that require data dependence are given as examples, such as vectorization translating serial code into vector. Software parallelism is a function of algorithm, programming style, and compiler optimization. Teaching parallel computing and dependence analysis with.
However, in more complex cases involving triangular or trapezoidal loop regions, symbolic. The data dependence graph is a powerful means for designing and analyzing parallel algorithms. Array dependence analysis enables optimization for parallelism in programs involving arrays. A data dependence results from multiple uses of the same locations in storage by different tasks. 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. Keywordsdata dependence, profiling, program analysis, par allelization, parallel programming. Function level parallelism driven by data dependencies core.
Possibility of a hazard order in which results must be. Gcd test use diophantine equationif failed, no data dependences, otherwise, continue. Traditional dependence profiling approaches introduce a tremendous amount of. Bo zhao, zhen li, ali jannesari, felix wolf, weiguo wu. For some classes of programs, static analysis and automatic parallelization is feasible 5, but with the current stateoftheart, most soft ware requires manual. Data dependence true, anti, output dependence source and sink distance vector, direction vector relation between reordering transformation and direction. Transformations that involve both control and data dependence cannot be specified in a consistent manner with this form, however, since control is. 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. Publications software analytics and pervasive parallelism lab. Identify the presence of crossiteration datadependences traditional analysis is inadequate for parallelization. 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.
Data dependence analysis for automatic parallelization of sequential tree codes is discussed. Dependencies are one of the primary inhibitors to parallelism. According to the new order of loops, exchange the elements in the direction vectorsto derive the new direction vectors. 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. Data dependence definition given two memory references, there exists a dependence between them if the three following conditions hold. 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. An efficient datadependence profiler for sequential and parallel. Pdf function level parallelism driven by data dependencies. Possibility of a hazard negative sideeffect if not in order the required order of instructions upper bound on achievable parallelism. Data dependence computation in parallel and vector constructs as well as serial do loops. Solving ilp for data dependence does data dependence exist in a loop. They reference the same array cell one of them is a write the two associated statements are executed two memory accesses and are data. 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.
The basic idea of prospector is dividing the work between software tools and programmers to maximize the overall performance bene. Data dependence computation in parallel and vector constructs as well as serialdo loops is covered. 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. Distributed memory communicate required data at synchronization points. Instructionlevel parallelism an overview sciencedirect. 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.