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1 edition of Static scheduling of conditional branches in parallel programs found in the catalog.

Static scheduling of conditional branches in parallel programs

by Robert Tyler George

  • 251 Want to read
  • 22 Currently reading

Published by Naval Postgraduate School, Available from National Technical Information Service in Monterey, Calif, Springfield, Va .
Written in English

    Subjects:
  • SCHEDULING,
  • PARALLEL PROCESSING

  • About the Edition

    The problem of scheduling parallel program tasks on multiprocessor systems is known to be NP-complete in its general form. When non-determinism is added to the scheduling problem through loops and conditional branching, an optimal solution is even harder to obtain. The intractability of obtaining an optimal solution for the general scheduling problem has led to the introduction of a large number of scheduling heuristics, These heuristics consider many real world factors, such as communication overhead, target machine topology, and the tradeoff between exploiting the parallelism in a parallel program and the resulting scheduling overhead. We present the probabilistic merge heuristic--in which a unified schedule of all possible execution instances is generated by successively scheduling tasks in order of their execution probabilities. When a conditional task is scheduled, we first attempt to merge the task with the time slot of a previously scheduled task which is a member of a different execution instance. We have found that the merge scheduler produces schedules which are 10% faster than previous techniques. More importantly, however, we show that the probabilistic merge heuristic is significantly more scalable -- being able to schedule branch and precedence graphs which exceed 50 modes.

    Edition Notes

    Statement Robert Tyler George
    The Physical Object
    Paginationviii, 55 p. ;
    Number of Pages55
    ID Numbers
    Open LibraryOL25267115M

    N2 - In VLIW (Very Long Instruction Word) compiler, one of the most important issue is how to handle conditional branches, because control dependences are caused by conditional branches and limit the scope of scheduling. This paper proposes the efficient method of eliminating conditional branches. If you could find a real program where bad branch prediction costs more than 1%-2% slowdown, I'd be very surprised. Performance counters or other tools that tell you where to find branch mispredictions are indispensible. If you actually need to improve such code, I'd look into trace scheduling .

    A branch is an instruction in a computer program that can cause a computer to begin executing a different instruction sequence and thus deviate from its default behavior of executing instructions in order. Branch (or branching, branched) may also refer to the act of switching execution to a different instruction sequence as a result of executing a branch instruction. conditional variables. Thread attributes: scheduling parameters stack size detached state Mutex attributes: normal only a single thread is allowed to lock it. if a threads tries to lock it twice a deadlock occurs. recursive a thread can lock the mutex multiple time. each successive lock increments a counter and each successive release.

    Environment variables are another, easy way to modify the parallel program's behavior from the command line, before execution. Nested parallelism is allowed A dynamic change in the number of threads is allowed before a parallel region is entered Parallel Programming for Multicore Machines Using OpenMP and MPI. Static Scheduling • basic pipeline: single, in-order issue • first extension: multiple issue (superscalar) • second extension: scheduling instructions for more ILP • option #1: dynamic scheduling (by the hardware) • option #2: static scheduling (by the compiler).


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Static scheduling of conditional branches in parallel programs by Robert Tyler George Download PDF EPUB FB2

The problem of scheduling non-deterministic graphs arises in several situations in scheduling parallel programs, particularly in the cases of loops and conditional branching.

When scheduling loops in a parallel program, non-determinism arises because the number of loop iterations may not be known before the execution of the by: T1 - Static scheduling of conditional branches in parallel programs.

AU - El-Rewini, Hesham. AU - Ali, Hesham H. PY - /1. Y1 - /1. N2 - The problem of scheduling non-deterministic graphs arises in several situations in scheduling parallel programs, particularly in the cases of loops and conditional by: Static Scheduling of Conditional Branches in Parallel Programs Article (PDF Available) in Journal of Parallel and Distributed Computing 24(1)–54 January with 45 Reads.

Static Scheduling of Conditional Branches in Parallel Programs. The problem of scheduling parallel program tasks on multiprocessor systems is known to be NP-complete in its general form. When non-determinism is added to the scheduling problem through loops and conditional branching, an optimal solution is even harder to obtain.

The problem of scheduling non-deterministic graphs arises in several situations in scheduling parallel programs, particularly in the cases of loops and conditional branching. Static scheduling of conditional branches in parallel programs. By Robert Tyler George. Get PDF (3 MB) Abstract. Approved for public release; distribution is unlimitedThe problem of scheduling parallel program tasks on multiprocessor systems is known to be NP-complete in its general form.

and the tradeoff between exploiting the parallelism. A number of algorithms and software tools have been presented to deal with the task scheduling problem in parallel processing. This paper studies the static scheduling of general types of applications.

We focus on "conditional task scheduling", in which the task model may vary between each program execution, due to conditional branching within.

Static scheduling of a program represented by a directed task graph on a multiprocessor system to minimize the program completion time is a well-known problem in parallel processing. Since finding an optimal schedule is an NP-complete problem in general. Other statically-scheduled machines reduce the effects of conditional branches by scheduling both paths of a conditional branch in parallel [18].

This scheme requires hardware support to NOP the instructions along the non-taken branch path. Though this technique allows for some overlap between the schedules of the basic blocks before and after. Abstract: The problem of scheduling task graphs with conditional branching is considered one of the most difficult problems in scheduling parallel programs on multiprocessor computers.

The major problem in having branches in task graphs is the non-determinism, since the direction of a branch may be unknown until the program is midway in execution.

Multiple Issue for Static/Dynamic Scheduling • Issue in order Power PC (see figure in book) • Issue stage. Up to 4 instructions issued/cycle except if • When a conditional branch is decoded: – Save the current physical-logical mapping – Predict and proceed.

Abstract. We discuss the static scheduling and code generation problem for message passing architectures. Using ideas from successful manually written programs for message passing architectures we have developed several new automatic algorithms for scheduling and code generation.

One of critical issues affecting parallel system performance is the scheduling of parallel tasks onto available target processors. A number of algorithms and software tools have been presented to deal with the task scheduling problem in parallel processing.

This paper studies the static scheduling of general types of applications. Idea: More ISA support for static scheduling and parallelization Specify dependencies within and between VLIW instructions (explicitly parallel) + No lock-step execution + Static reordering of stores and loads + dynamic checking -- Hardware needs to perform dependency checking (albeit aided by software).

As a result, code motions across conditional branch boundaries and software pipelining can be incorporated in the same algorithm.

Examples of global scheduling algorithms that perform software pipelining include enhanced pipeline scheduling [93] and GURPR [94]. The latter of these methods is based on partial loop unrolling, after which a.

The nice thing with static scheduling is that OpenMP run-time guarantees that if you have two separate loops with the same number of iterations and execute them with the same number of threads using static scheduling, then each thread will receive exactly the same iteration range(s) in both parallel regions.

static scheduling and centralized and distributed dynamic scheduling for coarse grain task parallel processing depend-ing on parallelism of the source program and performance parameters of the target machines. Parallel threads are forked only once at the beginning of the program and joined only once at the end in this scheme to minimize fork/join.

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques advocates the viability of using fuzzy and annealing methods in solving scheduling problems for parallel computing systems.

The book proposes new techniques for both static and dynamic scheduling, using emerging paradigms that are inspired by natural phenomena such as fuzzy logic, mean-field annealing, and simulated. As hardware designs get increasingly complex and time-to-market constraints get tighter there is strong motivation for high-level synthesis (HLS).

HLS must efficiently handle both dataflow-dominate. Review: Branch Prediction Idea: Predict the next fetch address (to be used in the next cycle) Requires three things to be predicted at fetch stage: Whether the fetched instruction is a branch (Conditional) branch direction Branch target address (if taken) Observation: Target address remains the same for a conditional direct branch across dynamic instances.dependency on the callees, permitting parallel execution of the functions on independent nodes.

2The Need for Dynamic Scheduling of Dynamic Control Flow Despite the programming support for expressing dynamic computation, existing dataflow-based deep learning systems employ a static computation schedule derived prior to graph execution.

A.There really are three different kinds of branches: §Forward conditional branches-based on a run-time condition, the PC (Program Counter) is changed to point to an address forward in the instruction stream.

§Backward conditional branches-the PC is changed to point backward in the instruction stream. The branch is based on some condition, such as branching backwards to the beginning.