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Kipish [7]
3 years ago
9

Media applications that play audio or video files are part of a class of workkloads called "streaming" workloads (i.e., they bri

ng in large amounts of data but do not reuse much of it). Consider a video streaming workload that accesses a 512 KiB working set sequentially with the following word address stream:
0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ...

a) Assume a 64 KiB direct-mapped cache with a 32-byte block. What is the miss rate for the address stream above? How is this miss rate sensitive to the size of the cache or the working set? How would you categorize the misses this workload is experiencing, based on the 3C model?

b) Re-compute the miss rate when the cache block size is 16 bytes, 64 bytes, and 128 bytes. What kind of locality is this workoad exploiting?

c) "Prefetching" is a technique that leverages predictable address patterns to speculatively bring in additoinal cache blocks when a particular cache block is accessed. One example of prefetching is a stream buffer that prefetches sequentially adjacent cache blocks into a separate buffer when a particular cache block is brought in. If the data are found in the prefetch buffer, it is considered as a hit, moved into the cache, and the cache block is prefetched. Assume a two-entry stream buffer; and, assume that the cache latency is such that a cache block can be loaded before the computation of the previous cache block is completed. What is the miss rate for the address stream above?

Computers and Technology
1 answer:
emmasim [6.3K]3 years ago
8 0

Answer:

Following is the detailed answer as needed.

<h3>I hope it will help you!</h3>

Explanation:

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In probability theory and statistics, a shape parameter is a kind of numerical parameter of a parametric family of probability distributions.[1]

Specifically, a shape parameter is any parameter of a probability distribution that is neither a location parameter nor a scale parameter (nor a function of either or both of these only, such as a rate parameter). Such a parameter must affect the shape of a distribution rather than simply shifting it (as a location parameter does) or stretching/shrinking it (as a scale parameter does).

Contents
Estimation Edit

Many estimators measure location or scale; however, estimators for shape parameters also exist. Most simply, they can be estimated in terms of the higher moments, using the method of moments, as in the skewness (3rd moment) or kurtosis (4th moment), if the higher moments are defined and finite. Estimators of shape often involve higher-order statistics (non-linear functions of the data), as in the higher moments, but linear estimators also exist, such as the L-moments. Maximum likelihood estimation can also be used.

Examples Edit

The following continuous probability distributions have a shape parameter:

Beta distribution
Burr distribution
Erlang distribution
ExGaussian distribution
Exponential power distribution
Fréchet distribution
Gamma distribution
Generalized extreme value distribution
Log-logistic distribution
Inverse-gamma distribution
Inverse Gaussian distribution
Pareto distribution
Pearson distribution
Skew normal distribution
Lognormal distribution
Student's t-distribution
Tukey lambda distribution
Weibull distribution
Mukherjee-Islam distribution
By contrast, the following continuous distributions do not have a shape parameter, so their shape is fixed and only their location or their scale or both can change. It follows that (where they exist) the skewness and kurtosis of these distribution are constants, as skewness and kurtosis are independent of location and scale parameters.

Exponential distribution
Cauchy distribution
Logistic distribution
Normal distribution
Raised cosine distribution
Uniform distribution
Wigner semicircle distribution
See also Edit

Skewness
Kurtosis
Location parameter
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4 years ago
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