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In computer architecture, speedup is a process for increasing the performance between two systems processing the same problem. More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources. The notion of speedup was established by Amdahl’s law, which was particularly focused on parallel processing. However, speedup can be used more generally to show the effect on performance after any resource enhancement.
Its value is typically between 0 and 1. Programs with linear speedup and programs running on a single processor have an efficiency of 1, while many difficult-to-parallelize programs have efficiency such as 1/ln(s) that approaches 0 as the number of processors A = s increases.
In engineering contexts, efficiency curves are more often used for graphs than speedup curves, since
- all of the area in the graph is useful (whereas in speedup curves half of the space is wasted);
- it is easy to see how well the improvement of the system is working;
- there is no need to plot a “perfect speedup” curve.
In marketing contexts, speedup curves are more often used, largely because they go up and to the right and thus appear better to the less-informed.
Sometimes a speedup of more than A when using A processors is observed in parallel computing, which is called super-linear speedup. Super-linear speedup rarely happens and often confuses beginners, who believe the theoretical maximum speedup should be A when A processors are used.
One possible reason for super-linear speedup in low-level computations is the cache effect resulting from the different memory hierarchies of a modern computer: in parallel computing, not only do the numbers of processors change, but so does the size of accumulated caches from different processors. With the larger accumulated cache size, more or even all of the working set can fit into caches and the memory access time reduces dramatically, which causes the extra speedup in addition to that from the actual computation.
An analogous situation occurs when searching large datasets, such as the genomic data searched by BLAST implementations. There the accumulated RAM from each of the nodes in a cluster enables the dataset to move from disk into RAM thereby drastically reducing the time required by e.g. mpiBLAST to search it.
Super-linear speedups can also occur when performing backtracking in parallel: an exception in one thread can cause several other threads to backtrack early, before they reach the exception themselves.
Super-linear speedups can also occur in parallel implementations of branch-and-bound for optimization: the processing of one node by one processor may affect the work other processors need to do for the other nodes
Let S be the speedup of execution of a task and s the speedup of execution of the part of the task that benefits from the improvement of the resources of an architecture. Linear speedup or ideal speedup is obtained when S = s. When running an task with linear speedup, doubling then local speedup doubles the overall speedup. As this is ideal, it is considered very good scalability.
Efficiency is a metric of the utilization of the resources of the improved system defined as
We can also measure speedup in cycles per instruction (CPI) which is a latency. First, we execute the program with the standard branch predictor, which yields a CPI of 3. Next, we execute the program with our modified branch predictor, which yields a CPI of 2. In both cases the execution workload is the same and both architectures are not pipelined nor parallel. Using the speedup formula gives
We are testing the effectiveness of a branch predictor on the execution of a program. First, we execute the program with the standard branch predictor on the processor, which yields an execution time of 2.25 seconds. Next, we execute the program with our modified (and hopefully improved) branch predictor on the same processor, which produces an execution time of 1.50 seconds. In both cases the execution workload is the same. Using our speedup formula, we know
- ρ is the execution density (e.g., the number of stages in an instruction pipeline for a pipelined architecture);
- A is the execution capacity (e.g., the number of processors for a parallel architecture).
Latency is often measured in seconds per unit of execution workload. Throughput is often measured in units of execution workload per second. Another frequent unit of throughput is the instruction per cycle (IPC). Its reciprocal, the cycle per instruction (CPI), is another frequent unit of latency.
Speedup is dimensionless and defined differently for each type of quantity so that it is a consistent metric.
- v is the execution speed of the task;
- T is the execution time of the task;
- W is the execution workload of the task.
Throughput of an architecture is the execution rate of a task:
A slow computer is reported to be one of the most common complaints PC repair technicians hear when users bring in their PC’s for repair or service. This condition may be caused by a wide array of issues, including file fragmentation, invalid or corrupt entries in the Windows registry, incorrect system settings that interfere with proper operation, misconfigured internet connection settings that lead to slower connection speeds and a number of other factors.
Slow computer conditions can be fixed and prevented with special software that detects and eliminates common causes of PC slowdowns –download here
The article provides details on the symptoms, causes and ways to repair slow computer conditions.
Symptoms of slow computer conditions
The most common symptoms of a slow computer include increased time for PC startup and shutdown, slower application launching, application or whole system freezes, slower response times (which may be noticed by users of text editors, where typed characters may appear on the screen with a brief delay), application crashes that require program restarts or computer reboots. In the case of slow internet connection speed, the user may notice slower web browsing, lower file download or upload speeds, poor quality of web calls, delays in message delivery when using chat programs and a number of other slowness symptoms.
Causes of slow computer conditions
Among the most common causes of slow computer conditions are misconfigured system settings that require adjusting, heavy file fragmentation due to hard drives not being defragmented on a regular basis, presence of invalid or corrupt entries in the Windows registry that prevents applications or system components from operating properly. Slow web browsing, downloads and web call interruptions on a supposedly high-speed connection are usually caused by incorrect network settings.
Ways to repair slow computer conditions
Advanced PC users may be able to improve their computer’s speed by manually resolving the common causes of computer slowdown – by running defragmentation, adjusting system and internet connection settings, removing invalid keys from the Windows registry. However, since any manipulations with system settings and the registry always carry a risk of rendering the operating system unbootable, whenever a user is in any doubt of their technical skills or knowledge, they should only use special software that is meant to resolve common speed issues and repair the Windows registry without requiring any special skills.
Safe way to fix slow computer: