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The end of the road for communication speed is nowhere in sight [Lew2]. Optical communication technology does not seem to have show-stopping technological roadblocks that will threaten progress in the near future. Several research labs are already experimenting with gigabitper-second all-optical networking.
The biggest obstacle currently is not of a technical nature; it is the infrastructure. High-speed networking necessitates the rewiring of the information society from copper cables to optical fiber. This campaign is already underway. Communication adapters are already faster than the computing devices attached to them. In the past, inefficient software has been masked by slow links. But an extra 1, instructions show up instantly as degraded throughput on a Fast Ethernet adapter.
Today, very few computers are capable of saturating a high-speed link, and it is only going to get more difficult. Optical communication technology is now surpassing the growth rate of microprocessor speed.
The computer processor plus software is quickly becoming the new bottleneck, and it's going to stay that way. To make a long story short, software performance is important and always will be.
This one is not going away. As processor and communication technology march on, they redefine what "fast" means. They give rise to a new breed of bandwidth- and cycle-hungry applications that push the boundaries of technology. You never have enough horsepower. Software efficiency now becomes even more crucial than before. Whether the growth of processor speed is coming to an end or not, it will definitely trail communication speed.
This puts the efficiency burden on the software. Further advances in execution speed will depend heavily on the efficiency of the software, not just the processor. Terminology Before moving on, here are a few words to clarify the terminology. Space efficiency seeks to minimize the use of memory in a software solution.
Likewise, time efficiency seeks to minimize the use of processor cycles. Time efficiency is often represented in terms of response time and throughput. Other metrics include compile time and executable size. The rapidly falling price of memory has moved the topic of space efficiency for its own sake to the back burner. Corporate customers xii are not that concerned about space issues these days.
In our work with customers we have encountered concerns with run-time efficiency for the most part. Since customers drive requirements, we will adopt their focus on time efficiency. From here on, we will restrict performance to its time-efficiency interpretation. Generally we will look at space considerations only when they interfere with run-time performance, as in caching and paging.
In discussing time efficiency, we will often mention the terms "pathlength" and "instruction count" interchangeably. Both stand for the number of assembler language instructions generated by a fragment of code. In a RISC architecture, if a code fragment exhibits a reasonable "locality of reference" i. On CISC architectures it may average two or more, but in any event, poor instruction counts always indicate poor execution time, regardless of processor architecture.
A good instruction count is necessary but not sufficient for high performance. Consequently, it is a crude performance indicator, but still useful. It will be used in conjunction with time measurements to evaluate efficiency. Organization of This Book We start the performance tour close to home with a real-life example. This example will drive home some performance lessons that might very well apply to diverse scenarios.
This is what we pay for the power of OO support. The significance of this cost, the factors affecting it, and how and when you can get around it are discussed in Chapters 2, 3, and 4. Chapter 5 is dedicated to temporaries.
C programmers are not used to the C compiler generating significant overhead "under the covers. Memory management is the subject of Chapters 6 and 7. Allocating and deallocating memory on the fly is expensive.
Functions such as new and delete are designed to be flexible and general. They deal with variable-sized memory chunks in a multithreaded environment. As such, their speed is compromised. Oftentimes, you are in a position to make simplifying assumptions about your code that will significantly boost the speed of memory allocation and deallocation.
These chapters will discuss several simplifying assumptions that can be made and the efficient memory managers that are designed to leverage them. Inlining is probably the second most popular performance tip, right after passing objects by reference. It is not as simple as it sounds. The inline keyword, just like register, is just a hint that the compiler often ignores.
Situations in which inline is likely to be ignored and other unexpected consequences are discussed in Chapters 8, 9, and Performance, flexibility, and reuse seldom go hand-in-hand. The Standard Template Library is an attempt to buck that trend and to combine these three into a powerful component. We will examine the performance of the STL in Chapter Software performance cannot always be salvaged by a single "silver bullet" fix. Performance degradation is often a result of many small local inefficiencies, each of which is insignificant by itself.
It is the combination that results in a significant degradation. We divided the list into two sets: coding and design inefficiencies. In Chapter 13 we discuss various items of that nature. The second set contains design optimizations that are global in nature. Those optimizations modify code that is spread across the source code, and are the subject of Chapter Chapter 15 covers scalability issues, unique performance considerations present in a multiprocessor environment that we don't encounter on a uniprocessor.
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This chapter discusses design and coding issues aimed at exploiting parallelism. This chapter will also provide some help with the terminology and concepts of multithreaded programming and synchronization. We refer to thread synchronization concepts in several other places in the book. If your exposure to those concepts is limited, Chapter 15 should help level the playing field.
Chapter 16 takes a look at the underlying system. Top-notch performance also necessitates a rudimentary understanding of underlying operating systems and processor architectures.
Issues such as caching, paging, and threading are discussed here. The Tracing War Story Every software product we have ever worked on contained tracing functionality in one form or another.
Any time your source code exceeds a few thousand lines, tracing becomes essential. It is important for debugging, maintaining, and understanding execution flow of nontrivial software. You would not expect a trace discussion in a performance book but the reality is, on more than one occasion, we have run into severe performance degradation due to poor implementations of tracing. Even slight inefficiencies can have a dramatic effect on performance. It is simple and familiar. We don't have to drown you in a sea of irrelevant details in order to highlight the important issues.
Programmers can define a Trace object in each function that they want to trace, and the Trace class can write a message on function entry and function exit. The Trace objects will add extra execution overhead, but they will help a programmer find problems without using a debugger. This is definitely something your customers will not be able to do unless you jump on the free software bandwagon and ship them your source code.
Alternatively, you can control tracing dynamically by communicating with the running program. It is assumed that tracing will be turned on only during problem determination. During normal operation, tracing would be inactive by default, and we expect our code to exhibit peak performance. For that to happen, the trace overhead must be minimal. A typical trace statement will look something along the lines of t.
Even when tracing is off, we still must create the string argument that is passed in to the debug function. The overhead of creating and destroying those string and Trace objects is at best hundreds of instructions. In typical OO code where functions are short and call frequencies are high, trace overhead could easily degrade performance by an order of magnitude.
This is not a farfetched figment of our imagination. We have actually experienced it in a reallife product implementation. It is an educational experience to delve into this particular horror story in more detail. Our first attempt backfired due to atrocious performance.
Our Initial Trace Implementation Our intent was to have the trace object log event messages such as entering a function, leaving a function, and possibly other information of interest between those two events.
Trace objects popped up in most of the functions on the critical execution path. The insertion of Trace objects has slowed down performance by a factor of five. We are talking about the case when tracing was off and performance was supposed to be unaffected.
Function call overhead is a factor so we should inline short, frequently called functions. Copying objects is expensive. Prefer pass-by-reference over pass-by-value. Our initial Trace implementation has adhered to all three of these principles. We stuck by the rules and yet we got blindsided. It is the creation and eventual destruction of unnecessary objects that were created in anticipation of being used but are not.
The Trace implementation is an example of the devastating effect of useless objects on performance, evident even in the simplest use of a Trace object. Invoke the Trace constructor.
The Trace constructor invokes the string constructor to create the member string. Invoke the Trace destructor. The Trace destructor invokes the string destructor for the member string. When tracing is off, the string member object never gets used.
You could also make the case that the Trace object itself is not of much use either when tracing is off. All the computational effort that goes into the creation and destruction of those objects is a pure waste. Keep in mind that this is the cost when tracing is off. This was supposed to be the fast lane.
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So how expensive does it get? We are trying to isolate the performance factors one at a time. This is Version 1 see Figure 1. The performance cost of the Trace object. In other words, the speed of addOne has plummeted by a factor of more than This kind of overhead will wreak havoc on the performance of any software.
The cost of our tracing implementation was clearly unacceptable.
We had to regroup and come up with a more efficient implementation. The Recovery Plan The performance recovery plan was to eliminate objects and computations whose values get dropped when tracing is off. We started with the string argument created by addOne and given to the Trace constructor.
Forget the string object. This translated into a performance boost, as was evident in our measurement.
Execution time dropped from 3, ms to 2, ms see Figure 1. Impact of eliminating one string object. The second step is to eliminate the unconditional creation of the string member object contained within the Trace object.
From a performance perspective we have two equivalent solutions. One is to replace the string object with a plain char pointer. The other solution is to use composition instead of aggregation.
Instead of embedding a string subobject in the Trace object, we could replace it with a string pointer. The advantage of a string pointer over a string object is that we can delay creation of the string after we have verified that tracing was on.
Response time has dropped from 2, ms to ms see Figure 1. Impact of conditional creation of the string member. TE So we have arrived. We took the Trace implementation from 3, ms down to ms.
You may still contend that ms looks pretty bad compared to a ms execution time when addOne had no tracing logic at all. This is more than 3x degradation.
So how can we claim victory?
The point is that the original addOne function without trace did very little. It added one to its input argument and returned immediately. The addition of any code to addOne would have a profound effect on its execution time. If you add four instructions to trace the behavior of only two instructions, you have tripled your execution time. If addOne consisted of more complex computations, the addition of Trace would have been closer to being negligible.
In some ways, this is similar to inlining. The influence of inlining on heavyweight functions is negligible. Inlining plays a major role only for simple functions that are dominated by the call and return overhead. The functions that make excellent candidates for inlining are precisely the ones that are bad candidates for tracing. It follows that Trace objects should not be added to small, frequently executed functions. We call it "silent execution" as opposed to "silent overhead" because object construction and destruction are not usually overhead.
If the computations performed by the constructor and destructor are always necessary, then they would be considered efficient code inlining would alleviate the cost of call and return overhead. As we have seen, constructors and destructors do not always have such "pure" characteristics, and they can create significant overhead. However, it is seen less often in C because it lacks constructor and destructor support. Just because we pass an object by reference does not guarantee good performance.
Avoiding object copy helps, but it would be helpful if we didn't have to construct and destroy the object in the first place. Don't waste effort on computations whose results are not likely to be used. When tracing is off, the creation of the string member is worthless and costly.
Don't aim for the world record in design flexibility. All you need is a design that's sufficiently flexible for the problem domain. A char pointer can sometimes do the simple jobs just as well, and more efficiently, than a string. Eliminate the function call overhead that comes with small, frequently invoked function calls. Inlining the Trace constructor and destructor makes it easier to digest the Trace overhead.
Constructors and Destructors In an ideal world, there would never be a chapter dedicated to the performance implications of constructors and destructors. In that ideal world, constructors and destructors would have no overhead. They would perform only mandatory initialization and cleanup, and the average compiler would inline them.
That's the theory. Down here in the trenches of software development, the reality is a little different. We often encounter inheritance and composition implementations that are too flexible and too generic for the problem domain. They may perform computations that are rarely or never required. In practice, it is not surprising to discover performance overhead associated with inheritance and composition. Inheritance and composition involve code reuse. Oftentimes, reusable code will compute things you don't really need in a specific scenario.
Any time you call functions that do more than you really need, you will take a performance hit. Inheritance Inheritance and composition are two ways in which classes are tied together in an object-oriented design. In this section we want to examine the connection between inheritance-based designs and the cost of constructors and destructors. We drive this discussion with a practical example: the implementation of thread synchronization constructs. Thread synchronization constructs appear in varied forms.
The three most common ones are semaphore, mutex, and critical section. A semaphore provides restricted concurrency. It allows multiple threads to access a shared resource up to a given maximum.
When the maximum number of concurrent threads is set to 1, we end up with a special semaphore called a mutex MUTual EXclusion. A mutex protects shared resources by allowing one and only one thread to operate on the resource at any one time. A shared resource typically is manipulated in separate code fragments spread over the application's code. Take a shared queue, for example. The number of elements in the queue is manipulated by both enqueue and dequeue routines.
Modifying the number of elements should not be done simultaneously by multiple threads for obvious reasons. Modifying this variable must be done atomically.
The simplest application of a mutex lock appears in the form of a critical section. A critical section is a single fragment of code that should be executed only by one thread at a time. To achieve mutual exclusion, the threads must contend for the lock prior to entering the critical section. The thread that succeeds in getting the lock enters the critical section. Upon exiting the critical section, the thread releases the lock to allow other threads to enter.
In Win32, a critical section consists of one or more distinct code fragments of which one, and only one, can execute at any one time. The difference between a critical section and a mutex in Win32 is that a critical section is confined to a single process, whereas mutex locks can span process boundaries and synchronize threads running in separate processes.
We are just pointing it out to avoid confusion. In practice we have seen routines that consisted of hundreds of lines of code containing multiple return statements.
If a lock was obtained somewhere along the way, we had to release the lock prior to executing any one of the return statements.
As you can imagine, this was a maintenance nightmare and a sure bug waiting to surface. Large-scale projects may have scores of people writing code and fixing bugs. If you add a return statement to a line routine, you may overlook the fact that a lock was obtained earlier. That's problem number one. The second one is exceptions: If an exception is thrown while a lock is held, you'll have to catch the exception and manually release the lock.
Not very elegant. When an object reaches the end of the scope for which it was defined, its destructor is called automatically. You can utilize the automatic destruction to solve the lock maintenance problem. Encapsulate the lock in an object and let the constructor obtain the lock.
The destructor will release the lock automatically. If such an object is defined in the function scope 10 of a line routine, you no longer have to worry about multiple return statements. The compiler inserts a call to the lock destructor prior to each return statement and the lock is always released. A mutex allows only one thread to access a shared resource. Nesting Some constructs allow a thread to acquire a lock when the thread already holds the lock.
Other constructs will deadlock on this lock-nesting. Notify When the resource becomes available, some synchronization constructs will notify all waiting threads. This is very inefficient as all but one thread wake up to find out that they were not fast enough and the resource has already been acquired. A more efficient notification scheme will wake up only a single waiting thread.
19 Challenging Problems with Solutions in C (eBook)
It is very tempting, therefore, to translate this similarity into an inheritancebased hierarchy of lock classes that are rooted in a unifying base class. Its constructor and destructor are empty. The BaseLock class was intended as a root class for the various lock classes that were expected to be derived from it.
These distinct flavors would naturally be implemented as distinct subclasses of BaseLock. The LogSource object is meant to capture the filename and source code line number where the object was constructed.
When logging errors and trace information it is often necessary to specify the location of the information source. Our developers chose to encapsulate both in a LogSource object. The LogSource object captured the source file and line number at which the lock was fetched. This information may come in handy when debugging deadlocks. The tension between reuse and performance is a topic that keeps popping up. The OS should be able to boot, start a userland shell, and be extensible.
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Book Site. How many runways in a particular airport? Click here to find out. Venugopal, B. An Extensive Tutorial Frank B. Programming Fundamentals: Roberts By emphasizing modern programming concepts such as interfaces, abstraction, and encapsulation, the book provides an ideal foundation for further study of programming.
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In a RISC architecture, if a code fragment exhibits a reasonable "locality of reference" i. Several research labs are already experimenting with gigabitper-second all-optical networking. Miscellaneous Books.
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