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The trend that emerges from the list is the growing distance between processor nodes More distant nodes mean higher latencies, but closer nodes mean increased cost and complexity For example, programming for MPP architectures is expensive and not something just any developer can do well MPP architectures are likely nonexistent for Web applications (although some technologies, such as those for video streaming, may use them); however, many applications do run on SMP machines and clusters In addition to these basic architectures, there's the notion of on-chip parallelism, which has to do with the ability to extract parallelism from a set of instructions or concurrent processes or threads Most CPUs today do an excellent job of exploiting instruction-level parallelism (ILP), using techniques such as multiple issue, pipelining, and speculative execution to intelligently schedule and predict low-level machine instructions However, as processors have become more powerful, ILP techniques have actually leveled off For example, the deeply pipelined processors of today can schedule many instructions at once The problem is, which ones Studies show that branches typically occur once every five machine instructions Branch prediction and speculative execution can be employed, but they become less relevant as the size of the pipeline increases For example, if a deeply pipelined processor allows many concurrent instructions, CPUs are forced to engage in highly speculative behavior, making predictions based on predictions of predictions of and so on You get the idea: the deeper you go in a pipeline, the less valuable its contents become Newer architectures promote the trading of deeper pipelines for multiple pipelines and the increased use of multithreading This trend is a direct response to the limits of ILP as the size of processor pipelines increase The increased use of multiple threads gives rise to a new category of parallelism thread-level parallelism (TLP) Note that the emergence of TLP does not spell the death of ILP Instead, it is widely envisioned that hybrid processor designs, reaping the benefits from both ILP and TLP, will yield the best performance Finding the optimal tradeoff between ILP and TLP will likely remain an important issue in processor architecture research for years to come You may wonder: Why did multithreading emerge and why has it become a popular means for achieving parallelism In fact, there has always been the need for parallelism in computing Computer architecture research and scientific programming have generated interest in parallel architectures since the 1960s As you probably know, a great many mathematical operations and problems naturally lend themselves to parallel computation Matrix multiplication is a good example; it is one of many complex operations that consist of a natural set of independent internal subproblems that can be computed in parallel However, parallelism for mass-market, consumer-oriented software (which typically consists of less scientific computations) is a relatively new phenomenon There are two major reasons for this One has to do with the recent increase in computer networking Prior to the rise of the Internet, most consumers bought shrink-wrapped software that operated on only local data (ie, data on.

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disk) Of course, most applications today (in fact, all Web applications) involve a network Increased network use yields applications that tend to be I/O-bound, and can thus significantly benefit from increased parallelism Another reason has to do with the phenomenon of Web applications and the need for concurrent request processing Instead of traditional (local) applications that execute only on the machine owned by the client, more recent (Web) applications contain logic that executes on a remote server This remote server often needs to handle hundreds, if not thousands, of simultaneous requests Since each request is independent, there is great interest in parallelizing as much of this request processing as possible Advances in programming languages have also made threads more attractive The Java phenomenon is probably the primary example Only recently, programmers who could write threaded code were few and far between Java changed all that by making threads accessible, easy to manage, and relatively safe The result: more threaded software, which demands more TLP When you use Java threads for parallelism, you should carefully assess their value against the cost of the overhead involved This isn't to say that Java threads are expensive they aren't It can be more expensive not to create them However, it's important to understand when to use them specifically, the conditions in which they tend to improve efficiency Let's see how two threads compare to one for two very different tasks The first task will be a CPU-bound activity Suppose an application server needs to increment multiple counters 10,000,000 times each Is it faster to increment them by proceeding sequentially, incrementing each counter 10,000,000 times, or by proceeding in parallel Of course, there may be hundreds of concurrent transactions that a given application server processes as you'll see, however, the effects of parallel versus sequential approaches becomes clear in the early going To answer our counting question, we can write two Java programs, one that counts sequentially and one that counts in parallel The results are shown in Figure 4-5 Clearly, sequential counting is more efficient Figure 4-5 Sequential versus concurrent counting (CPU-bound activity).

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Topic 61: Tax Characteristics of Entities - 197 (1) Shareholders receive a Schedule K-1 (same as partnerships) to incorporate into their individual tax returns (2) The cash ow from the S Corporation is irrelevant to the computation of the shareholder s tax liability (3) Shareholders can also be employees of the corporation The employee and corporation pay Federal Insurance Contributions Act (FICA) payroll taxes, and the corporation withholds federal income tax The corporation issues a W-2 form to any shareholder/employee along with a K-1 Shareholders are not subject to SE tax E Adjusting the basis in a partnership (1) The basis in a partnership increases by ordinary business income, capital gains, and dividend income and decreases from ordinary business loss, capital loss, and cash distributions (2) If a partner is allocated income (taxable) but receives no cash distribution from that income, the partner is making an additional investment in the partnership The cash distribution in future years is considered a nontaxable return on investment Thus, cash distributions are generally nontaxable for pass-through entities (3) Partners may deduct their distributive share of partnership losses for the year However, this results in a reduction of their cost basis in the partnership interest (4) The basis cannot be reduced below zero If a partner s share of loss exceeds the basis, the excess cannot be deducted in the current year It can be carried forward to future years, but the basis must be restored in order to deduct the carryforward F Adjusting the basis of S Corporation stock (1) Shareholders increase the basis of stock by their share of the corporation s income and gain; they decrease the basis of stock by their share of the corporation s losses (2) The cash distribution is considered a nontaxable return of investment that reduces their stock basis Thus, cash distributions are generally nontaxable for pass-through entities (3) Losses are deductible to the extent of the owner s equity investment and debt obligation (4) In both cases, the basis cannot be reduced below zero For example, Steve has a basis in stock of $90,000 and a basis in debt of $30,000 Therefore, Steve has an equity investment and an investment as a corporate creditor If the corporation passes through a loss of $150,000 to Steve (re ected on his K-1), the most that can be deducted is $120,000 on his Form 1040 The $30,000 nondeductible loss is carried forward G Maximizing tax bene ts of start-up rms: Losses immediately pass through to partners or shareholders For a C Corporation, losses may be trapped at the entity level as loss carryforwards H Partnerships offer the ability to shift income to family members (1) By dividing income among a number of family members, the total tax burden may shrink to the extent that the division causes income to be taxed at a lower tax bracket (2) Family partnerships are common for shifting income, but the IRS closely restricts their use as income-shifting devices 4 Special taxes at entity level for ow-through entities A Built-in gains tax (1) When one corporation is acquired by another, there are limits in the extent to which the built-in gains of one corporation (gain corporation) can be used to offset the preacquisition losses of the other corporation If either corporation is a gain.

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