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Load balance and load balancing To execute a parallel program, the tasks must be mapped to UEs, and the UEs to PEs How the mappings are done can have a significant impact on the overall performance of a parallel algorithm It is crucial to avoid the situation in which a subset of the PEs is doing most of the work while others are idle Load balance refers to how well the work is distributed among PEs Load balancing is the process of allocating work to PEs, either statically or dynamically, so that the work is distributed as evenly as possible Synchronization In a parallel program, due to the nondeterminism of task scheduling and other factors, events in the computation might not always occur in the same order For example, in one run, a task might read variable x before another task reads variable y; in the next run with the same input, the events might occur in the opposite order In many cases, the order in which two events occur does not matter In other situations, the order does matter, and to ensure that the program is correct, the programmer must introduce synchronization to enforce the necessary ordering constraints The primitives provided for this purpose in our selected environments are discussed in the Implementation Mechanisms design space (Section 63) Synchronous versus asynchronous We use these two terms to qualitatively refer to how tightly coupled in time two events are If two events must happen at the same time, they are synchronous; otherwise they are asynchronous For example, message passing (that is, communication between UEs by sending and receiving messages) is synchronous if a message sent must be received before the sender can continue Message passing is asynchronous if the sender can continue its computation regardless of what happens at the receiver, or if the receiver can continue computations while waiting for a receive to complete Race conditions A race condition is a kind of error peculiar to parallel programs It occurs when the outcome of a program changes as the relative scheduling of UEs varies Because the operating system and not the programmer controls the scheduling of the UEs, race conditions result in programs that potentially give different answers even when run on the same system with the same data Race conditions are particularly difficult errors to debug because by their nature they cannot be reliably reproduced Testing helps, but is not as effective as with sequential programs: A program may run correctly the first thousand times and then fail catastrophically on the thousand and first execution and then run again correctly when the programmer attempts to reproduce the error as the first step in debugging Race conditions result from errors in synchronization If multiple UEs read and write shared variables, the programmer must protect access to these shared variables so the reads and writes occur in a valid order regardless of how the tasks are interleaved When many variables are shared or when they are accessed through multiple levels of indirection, verifying by inspection that no race conditions exist can be very difficult Tools are available that help detect and fix race conditions, such as ThreadChecker from Intel Corporation, and the problem remains an area of active and important research [NM92] Deadlocks Deadlocks are another type of error peculiar to parallel programs A deadlock occurs when there is a cycle of tasks in which each task is blocked waiting for another to proceed Because all are waiting for another task to do something, they will all be blocked forever As a simple example, consider two tasks in a message passing environment Task A attempts to receive a message from task B, after which A will reply by sending a message of its own to task B Meanwhile, task B attempts to.

coroutine1() Waiting Process "a" Process "a" Waiting Process "b" Process "b" Waiting Waiting Waiting Waiting Waiting Finished coroutine2() Waiting Waiting Process "a" Process "a" Process "a" Process "a" ("b" pending) Process "a" ("b" pending) Process "b" Process "b" Process "b" Waiting Finished coroutine3() Waiting Waiting Waiting Process "a" Process "a" Process "a" Process "b" Process "b" Waiting Waiting Waiting Finished

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to process, if one is ever waiting (because it nishes rst), the others continue to work, which minimizes processor idle time Once we are nished using the coroutines we call close() on each of them; this stops them from waiting for more data, which means they won t consume any more processor time To create a coroutine in Python, we simply create a function that has at least one yield expression normally inside an in nite loop When a yield is reached the coroutine s execution is suspended waiting for data Once the data is received the coroutine resumes processing (from the yield expression onward), and when it has nished it loops back to the yield to wait for more data While one or more coroutines are suspended waiting for data, another one can execute This can produce greater throughput than simply executing functions one after the other linearly We will show how performing independent operations works in practice by applying several regular expressions to the text in a set of HTML les The purpose is to output each le s URLs and level 1 and level 2 headings We ll start by looking at the regular expressions, then the creation of the coroutine matchers , and then we will look at the coroutines and how they are used

receive a message from task A, after which B will send a message to A Because each task is waiting for the other to send it a message first, both tasks will be blocked forever Fortunately, deadlocks are not difficult to discover, as the tasks will stop at the point of the deadlock

URL_RE = recompile(r"""href=( P<quote>['"])( P<url>[^\1]+ )""" r"""( P=quote)""", reIGNORECASE) flags = reMULTILINE|reIGNORECASE|reDOTALL H1_RE = recompile(r"<h1>( P<h1>+ )</h1>", flags) H2_RE = recompile(r"<h2>( P<h2>+ )</h2>", flags)

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