CN113782086A - Method and device for carrying out hierarchical evaluation on PMEM memory - Google Patents
Method and device for carrying out hierarchical evaluation on PMEM memory Download PDFInfo
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Abstract
The invention provides a method and a device for carrying out grading evaluation on a PMEM memory, wherein the method comprises the following steps: s1, carrying out test item layering on the PMEM memory according to a working mode; s2, acquiring a PMEM memory sample set, testing according to layered test items, and recording layered test results; s3, calculating a probability distribution function of a test result in the PMEM memory sample set; s4, dividing level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index; and S5, carrying out grading evaluation on each PMEM memory according to the evaluation indexes. The invention realizes the grading evaluation of the PMEM memory so as to carry out PMEM memory quality sequencing, function recommendation and risk early warning.
Description
Technical Field
The invention belongs to the technical field of memory testing, and particularly relates to a method and a device for carrying out hierarchical evaluation on a PMEM memory.
Background
With the development of memory technology, new memory types are produced. PMEM is a new type of memory today, also known as DPCM or DCPMM. The sub-products of PMEM memory include AEP and BPS, and CPS to be released in 2021, and each generation of PMEM corresponds to each generation of platform.
Compared with the common memory, the PMEM memory has the following characteristics: and as a large-capacity memory, the data and process loading time can be saved after the server is restarted. And secondly, the total cost is reduced, the CPU is excessive, the number of devices is intelligently increased in an application scene with insufficient memory capacity, software authorization is increased, equipment cabinets are added, energy consumption is increased, and meanwhile, the original dispersed services can be combined. Third, the PMEM memory can provide relatively large capacity while providing read and write speeds and latencies close to those of DRAM, which are incomparable with conventional SSD. Fourth, the available single bar has large capacity and is compatible with DDR4 slot.
The PMEM memory has various modes which can be set to meet the use of various functions, and the memory modes of the PMEM mainly include the following modes: first, the memory mode is suitable for the memory with large capacity. And secondly, applying a direct access mode, wherein the PMEM memory has persistence as storage in the mode. And thirdly, a mixed mode, namely a part of the memory is filled with a memory mode, and a part of the memory is in an application direct access mode, and in the mode, the application can use high-performance storage to avoid transmitting data back and forth on the I/O bus.
The testing method of the PMEM memory is still not mature, the existing testing method can only simply carry out evaluation according to certain testing items, single evaluation cannot be carried out on a single PMEM memory, and good, medium and poor grade evaluation cannot be carried out on a batch of PMEM memory products.
Therefore, it is very necessary to provide a method and an apparatus for performing a hierarchical evaluation on a PMEM memory, which is directed to the above-mentioned drawbacks of the prior art.
Disclosure of Invention
Aiming at the defects that the existing memory test method in the prior art cannot perform single evaluation on a single PMEM memory and cannot perform good, medium and poor grade evaluation on a batch of PMEM memory products, the invention provides a method and a device for performing grade evaluation on the PMEM memory, and aims to solve the technical problems.
In a first aspect, the present invention provides a method for performing a hierarchical evaluation on a PMEM memory, including the following steps:
s1, layering test items of the PMEM memory according to a working mode;
s2, acquiring a PMEM memory sample set, testing according to layered test items, and recording layered test results;
s3, calculating a probability distribution function of a test result in the PMEM memory sample set;
s4, dividing level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index;
and S5, carrying out grading evaluation on each PMEM memory according to the evaluation indexes.
Further, the step S1 specifically includes the following steps:
s11, acquiring a large class to which the PMEM memory belongs;
s12, obtaining a test mode of each main type of the PMEM memory;
s13, obtaining a test sub item of each test mode under each major category of the PMEM;
and S14, layering all the test sub-items of the PMEM according to the large class and the test mode. And layering the PMEM memory according to the categories, the test modes and the test sub-items under each test mode, so that the test is comprehensive, and the test results can be conveniently counted according to layers.
Further, the specific step of S2 is as follows:
s21, acquiring a PMEM memory sample set;
s22, testing each testing sub item of each PMEM memory in the PMEM memory sample set for a preset number of times, and recording each testing result;
s23, judging the test of each test sub-item, and judging whether the test meets the Bernoulli distribution function;
if yes, go to step S24;
if not, eliminating the PMEM memory corresponding to the test sub-item;
and S24, removing the maximum value and the minimum value of all the test results of each test sub-item, and then taking the average value as the test result of the corresponding test sub-item. And (4) primarily screening the test result through a Bernoulli distribution function, and filtering out gross errors.
Further, the step S3 specifically includes the following steps:
s31, counting the test result of the test sub-item of each PMEM memory in the PMEM memory sample set, and setting the test result as a test value;
s32, calculating the mean value and the variance of all test values in the PMEM memory sample set;
and S33, generating a Gaussian distribution function which takes the test values as variables and the probabilities as output values by taking the mean value of all the test values as offset and the variance of all the test values as amplitude. The gaussian distribution function embodies the probability distribution of all test results in the PMEM memory sample set.
Further, the step S4 specifically includes the following steps:
s41, dividing the Gaussian distribution function into four intervals according to a curve symmetry axis and a turning point;
s42, dividing four intervals into four levels according to the variable size, setting weight for each level, and setting the interval weight with a large variable value to be larger than the interval weight with a small variable value;
s43, counting the number of the test values in each level interval in each PMEM, and carrying out weighted summation according to set weights to obtain the evaluation index of each PMEM. And classifying the test result according to the probability distribution function, wherein the interval with large variable values has large influence on the level, the weight of the interval with large variable values is increased, the interval with small variable values has small influence on the level, and the weight of the interval with small variable values is reduced.
Further, the step S5 specifically includes the following steps:
s51, sequencing the PMEM memories in the PMEM memory sample set according to the evaluation indexes;
and S52, the performance of the PMEM memory with large evaluation index value is higher than that of the PMEM memory with low evaluation index value. And the evaluation index sequencing realizes the simplest hierarchical evaluation of the PMEM memory.
Further, the step S5 specifically includes the following steps:
S51A, creating a PMEM memory classification algorithm model;
S52A, obtaining evaluation indexes and actual index expressions of PMEM memories in a PMEM memory sample set;
S53A, training a PMEM memory classification algorithm model through evaluation indexes and actual index expressions of each PMEM memory;
S54A, performing prediction grading evaluation on the PMEM memory in the new PMEM memory sample set by using a PMEM memory classification algorithm model to obtain a classification result;
and S55A, acquiring time index performance of the PMEM memory in a new PMEM memory sample set, comparing the time index performance with a classification result of prediction grading evaluation, and correcting a PMEM memory classification algorithm model. By creating a PMEM memory classification algorithm model and continuously training the PMEM memory classification algorithm model, the classification evaluation prediction of the PMEM memory can be realized.
Further, the PMEM memory classification algorithm model adopts one or more of a Bayesian algorithm, a random forest algorithm or a mean algorithm.
Further, setting classification result parameters in the PMEM memory classification algorithm model;
when the number of samples in the PMEM memory sample set is smaller than a set threshold, setting the weight of the classification result parameter to be 0;
when the sum of the number of the samples in the PMEM memory sample set and the number of the samples in the new PMEM memory sample set reaches a set threshold value, increasing the weight of classification result parameters;
and when the test requirement is to perform grading evaluation on a certain function or a certain mode of the PMEM memory, setting the weight of the parameter corresponding to the function or the mode which is not to be tested in the PMEM memory classification algorithm model to be 0. When the number of samples in the PMEM memory sample set is small, the weight of the classification result parameter can be set to be 0, and the weight of the response is increased after the PMEM memory classification algorithm model is accurately trained; when a hierarchical evaluation is performed for a certain function or a certain mode of the PMEM memory, for example, only the hierarchy in the memory mode is considered, the parameter weight of other modes may be set to 0.
In a second aspect, the present invention provides an apparatus for performing a hierarchical evaluation on a PMEM memory, including:
the test item layering module is used for layering the test items of the PMEM memory according to the working mode;
the layered test module is used for acquiring a PMEM memory sample set, testing according to layered test items and recording layered test results;
the probability distribution function calculation module is used for calculating the probability distribution function of the test result in the PMEM memory sample set;
the evaluation index calculation module is used for dividing the level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index;
and the PMEM memory grading evaluation module is used for grading and evaluating each PMEM memory according to the evaluation index.
The beneficial effect of the invention is that,
the method and the device for carrying out the grading evaluation on the PMEM memories, provided by the invention, can realize the grading evaluation on the PMEM memories, can carry out quality sequencing on a batch of PMEM memories according to evaluation indexes, also can recommend the most suitable ones of the batch of PMEM memories according to a certain functional requirement, and can also carry out risk early warning on the PMEM memories according to the evaluation indexes.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a first flowchart illustrating a method for performing hierarchical evaluation on a PMEM memory according to the present invention.
Fig. 2 is a schematic flow chart of a method for performing hierarchical evaluation on a PMEM memory according to the present invention.
Fig. 3 is a third schematic flowchart of the method for performing hierarchical evaluation on the PMEM memory according to the present invention.
Fig. 4 is a schematic diagram of the device for performing hierarchical evaluation on the PMEM memory according to the present invention.
FIG. 5 is a diagram showing Gaussian distribution functions in example 4 of the present invention.
In the figure, 1-test item hierarchy module; 2-a layered test module; 3-a probability distribution function calculation module; 4-evaluation index calculation module; 5-PMEM memory grading evaluation module.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the present invention provides a method for performing a hierarchical evaluation on a PMEM memory, comprising the following steps:
s1, layering test items of the PMEM memory according to a working mode;
s2, acquiring a PMEM memory sample set, testing according to layered test items, and recording layered test results;
s3, calculating a probability distribution function of a test result in the PMEM memory sample set;
s4, dividing level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index;
and S5, carrying out grading evaluation on each PMEM memory according to the evaluation indexes.
The method for carrying out grading evaluation on the PMEM memories, provided by the invention, realizes the grading evaluation on the PMEM memories, can carry out quality sequencing on a batch of PMEM memories according to evaluation indexes, also can recommend the most suitable ones of the batch of PMEM memories according to a certain functional requirement, and also can carry out risk early warning on the PMEM memories according to the evaluation indexes.
Example 2:
as shown in fig. 2, the present invention provides a method for performing a hierarchical evaluation on a PMEM memory, which includes the following steps:
s1, layering test items of the PMEM memory according to a working mode; the method comprises the following specific steps:
s11, acquiring a large class to which the PMEM memory belongs;
s12, obtaining a test mode of each main type of the PMEM memory;
s13, obtaining a test sub item of each test mode under each major category of the PMEM;
s14, layering all test sub items of the PMEM memory according to the large class and the test mode; layering the PMEM memory according to the categories, the test modes and the test sub-items under each test mode, testing comprehensively, and facilitating the subsequent statistics of test results according to the layers;
s2, acquiring a PMEM memory sample set, testing according to layered test items, and recording layered test results; the method comprises the following specific steps:
s21, acquiring a PMEM memory sample set;
s22, testing each testing sub item of each PMEM memory in the PMEM memory sample set for a preset number of times, and recording each testing result;
s23, judging the test of each test sub-item, and judging whether the test meets the Bernoulli distribution function;
if yes, go to step S24;
if not, eliminating the PMEM memory corresponding to the test sub-item;
s24, removing the maximum value and the minimum value of all test results of each test sub-item, and then taking the average value as the test result of the corresponding test sub-item; primarily screening the test result through a Bernoulli distribution function, and filtering out a coarse error;
s3, calculating a probability distribution function of a test result in the PMEM memory sample set; the method comprises the following specific steps:
s31, counting the test result of the test sub-item of each PMEM memory in the PMEM memory sample set, and setting the test result as a test value;
s32, calculating the mean value and the variance of all test values in the PMEM memory sample set;
s33, generating a Gaussian distribution function which takes the test values as variables and the probabilities as output values by taking the mean values of all the test values as offsets and the variances of all the test values as amplitudes; the Gaussian distribution function embodies the probability distribution condition of all test results in the PMEM memory sample set;
s4, dividing level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index; the method comprises the following specific steps:
s41, dividing the Gaussian distribution function into four intervals according to a curve symmetry axis and a turning point;
s42, dividing four intervals into four levels according to the variable size, setting weight for each level, and setting the interval weight with a large variable value to be larger than the interval weight with a small variable value;
s43, counting the number of the test values in each level interval in each PMEM, and carrying out weighted summation according to set weights to obtain an evaluation index of each PMEM; grading the test result according to a probability distribution function, wherein the section with a large variable value has a large influence on the grade, the weight of the section with a large variable value is increased, the section with a small variable value has a small influence on the grade, and the weight of the section with a small variable value is reduced;
s5, carrying out grading evaluation on each PMEM memory according to the evaluation indexes;
the method comprises the following specific steps:
s51, sequencing the PMEM memories in the PMEM memory sample set according to the evaluation indexes;
s52, the performance of the PMEM memory with large evaluation index value is higher than that of the PMEM memory with low evaluation index value; and the evaluation index sequencing realizes the simplest hierarchical evaluation of the PMEM memory.
Example 3:
as shown in fig. 3, the present invention provides a method for performing a hierarchical evaluation on a PMEM memory, which includes the following steps:
s1, layering test items of the PMEM memory according to a working mode; the method comprises the following specific steps:
s11, acquiring a large class to which the PMEM memory belongs;
s12, obtaining a test mode of each main type of the PMEM memory;
s13, obtaining a test sub item of each test mode under each major category of the PMEM;
s14, layering all test sub items of the PMEM memory according to the large class and the test mode; layering the PMEM memory according to the categories, the test modes and the test sub-items under each test mode, testing comprehensively, and facilitating the subsequent statistics of test results according to the layers;
s2, acquiring a PMEM memory sample set, testing according to layered test items, and recording layered test results; the method comprises the following specific steps:
s21, acquiring a PMEM memory sample set;
s22, testing each testing sub item of each PMEM memory in the PMEM memory sample set for a preset number of times, and recording each testing result;
s23, judging the test of each test sub-item, and judging whether the test meets the Bernoulli distribution function;
if yes, go to step S24;
if not, eliminating the PMEM memory corresponding to the test sub-item;
s24, removing the maximum value and the minimum value of all test results of each test sub-item, and then taking the average value as the test result of the corresponding test sub-item; primarily screening the test result through a Bernoulli distribution function, and filtering out a coarse error;
s3, calculating a probability distribution function of a test result in the PMEM memory sample set; the method comprises the following specific steps:
s31, counting the test result of the test sub-item of each PMEM memory in the PMEM memory sample set, and setting the test result as a test value;
s32, calculating the mean value and the variance of all test values in the PMEM memory sample set;
s33, generating a Gaussian distribution function which takes the test values as variables and the probabilities as output values by taking the mean values of all the test values as offsets and the variances of all the test values as amplitudes; the Gaussian distribution function embodies the probability distribution condition of all test results in the PMEM memory sample set;
s4, dividing level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index; the method comprises the following specific steps:
s41, dividing the Gaussian distribution function into four intervals according to a curve symmetry axis and a turning point;
s42, dividing four intervals into four levels according to the variable size, setting weight for each level, and setting the interval weight with a large variable value to be larger than the interval weight with a small variable value;
s43, counting the number of the test values in each level interval in each PMEM, and carrying out weighted summation according to set weights to obtain an evaluation index of each PMEM; grading the test result according to a probability distribution function, wherein the section with a large variable value has a large influence on the grade, the weight of the section with a large variable value is increased, the section with a small variable value has a small influence on the grade, and the weight of the section with a small variable value is reduced;
s5, carrying out grading evaluation on each PMEM memory according to the evaluation indexes; the method comprises the following specific steps:
S51A, creating a PMEM memory classification algorithm model; the PMEM memory classification algorithm model adopts one or more of Bayesian algorithm, random forest algorithm or mean algorithm;
S52A, obtaining evaluation indexes and actual index expressions of PMEM memories in a PMEM memory sample set;
S53A, training a PMEM memory classification algorithm model through evaluation indexes and actual index expressions of each PMEM memory;
S54A, performing prediction grading evaluation on the PMEM memory in the new PMEM memory sample set by using a PMEM memory classification algorithm model to obtain a classification result;
S55A, acquiring time index performance of a PMEM memory in a new PMEM memory sample set, comparing the time index performance with a classification result of prediction grading evaluation, and correcting a PMEM memory classification algorithm model; by creating a PMEM memory classification algorithm model and continuously training the PMEM memory classification algorithm model, the classification evaluation prediction of the PMEM memory can be realized.
In a certain embodiment, a classification result parameter is set in a PMEM memory classification algorithm model;
when the number of samples in the PMEM memory sample set is smaller than a set threshold, setting the weight of the classification result parameter to be 0;
when the sum of the number of the samples in the PMEM memory sample set and the number of the samples in the new PMEM memory sample set reaches a set threshold value, increasing the weight of classification result parameters;
when the test requirement is to carry out grading evaluation on a certain function or a certain mode of the PMEM memory, setting the weight of the parameter corresponding to the function or the mode which is not to be tested in the PMEM memory classification algorithm model to be 0; when the number of samples in the PMEM memory sample set is small, the weight of the classification result parameter can be set to be 0, and the weight of the response is increased after the PMEM memory classification algorithm model is accurately trained; when a hierarchical evaluation is performed for a certain function or a certain mode of the PMEM memory, for example, only the hierarchy in the memory mode is considered, the parameter weight of other modes may be set to 0.
The original PMEM memory test evaluation method comprises the following steps: one is to follow the manufacturer test guidelines exactly. According to the test document or the manufacturer personnel suggestion provided by the manufacturer; and secondly, adopting the same test method as the common memory. Such as stability testing, performance testing, RMT testing, etc.; thirdly, the testing method is the same as that of the common hard disk. Such as stability testing, performance testing, signal testing, etc.; and fourthly, testing only aiming at a certain function of the PMEM, such as a memory mode and the like. The second method and the third method both adopt a common method for testing, and do not test the PMEM new product, and the second method and the third method can only evaluate the product to be qualified and unqualified, and the evaluation method of the first method can not evaluate the product; the first method completely follows the test guidance of manufacturers, and the test evaluation method has low reliability and cannot avoid the phenomenon that some test items are diluted for hiding some defects; the condition of the PMEM memory cannot be comprehensively measured, and if the condition is too single, a batch of PMEM memories can be caused, so that PMEM memories with different characteristics are omitted, and only one characteristic PMEM memory is reserved; and the common characteristics of the four methods are that the whole PMEM cannot be classified and classified, and the PMEM cannot be effectively tested.
The PMEM memory is evaluated in a grading way by combining data processing through the Bernoulli distribution function and a PMEM memory classification algorithm model, the data processing and the algorithm model are iterated and verified mutually, a processing way with a simple mathematical characteristic is abandoned, the processing is carried out in a probability way, and not only can the integral evaluation be carried out, but also the specific item can be evaluated.
Example 4:
in the above embodiment 3, the step S1 is to perform test item layering on the PMEM memory according to the working mode, and the specific steps are as follows:
acquiring the main class of the PMEM memory, including an AEP memory, a BPS memory and a CPS memory; wherein, AEP is a mature PMEM memory product, BPS is a memory product in a development stage, and CPS memory is a PMEM memory product planned in the future;
each major category comprises three basic modes, namely a memory mode, an application direct access mode and a mixed mode, wherein the application direct access mode comprises two sub-modes, namely an application direct access interleaving mode and an application direct access non-interleaving mode; the mixed mode also comprises two modes, namely a mixed mode of a memory mode and an application direct access interleaving mode, and a mixed mode of the memory mode and the application direct access non-interleaving mode;
take AEP memory as an example, wherein five seed patterns: the memory mode, the application direct reading mode, the mixed mode of the memory mode and the application direct reading interweaving mode and the mixed mode of the memory mode and the application direct reading non-interweaving mode are respectively marked as an A mode, a B mode, a C mode, a D mode and an E mode;
the test sub items in the A mode comprise an RMT test, a stability test, a performance test and an information check test; the RMT test is an identification memory test;
the test sub-items in the mode B comprise a new function test, a stability test, a performance test and an information inspection test;
the test sub-items in the mode C comprise a new function test, a stability test, a performance test and an information inspection test;
the test sub-items in the D mode comprise a new function test, a stability test, a performance test and an information inspection test;
the test sub-items in the E mode comprise a new function test, a stability test, a performance test and an information inspection test;
the result of the RMT test in mode A is designated A11; the stability test comprises a restart test, an AC power-off test and a DC power-off test, and test results are recorded as A21, A22 and A23; the performance test comprises MLC test and PTU test, and the test results are marked as A31 and A32; the information inspection test result is recorded as A41; the MLC test is the memory latency test of intel;
performing RMT test in mode A for 10 times, and recording the results as A110, A111, A112, A113, A114, A115, A116, A117, A118 and A119;
judging the result of the RMT test by Bernoulli distribution, if P (X ═ fail) > 0; then the AEP memory is directly eliminated;
if P (X ═ fail) ═ 0, then the RMT tests 10 test results, with the maximum and minimum values removed. The remaining 8 values are averaged and assigned to a 11;
the other tests in mode a are also processed with reference to the step RMT test, assigned to a21, a22, a23, a31, a32, a41, respectively;
B. c, D, E the mode carries out data processing according to the A mode;
the parameters finally output in the B mode are assigned to B11, B12, B21, B22, B23, B31, B32 and B41; the new function test in the B mode comprises an ADR test and a safety erasure test which are respectively assigned to B11 and B12;
the parameters finally output in the C mode are assigned to C11, C12, C21, C22, C23, C31, C32 and C41; the new function test in the C mode comprises an ADR test and a safety erasure test which are respectively assigned to B11 and B12;
the parameters finally output in the D mode are assigned to D11, D12, D21, D22, D23, D31, D32 and D41; the new function test in the D mode comprises an ADR test and a safe erasure test which are respectively assigned to D11 and D12;
the parameters finally output in the E mode are assigned to E11, E12, E21, E22, E23, E31, E32 and E41; the new function test in the E mode comprises an ADR test and a safety erasure test which are respectively assigned to E11 and E12;
taking 10 AEPs as examples, they are respectively referred to as P0, P1, P2, P3, P4, P5, P6, P7, P8 and P9.
Test results for P0 may be recorded as P0a11, P0a21, P0a22, P0a23, P0a31, P0a32, P0a41, P0B11, P0B12, P0B21, P0B22, P0B23, P0B31, P0B32, P0B41, P0C11, P0C12, P0C21, P0C22, P0D22, P0E22, P0E22, P22;
p1, P2, P3, P4, P5, P6, P7, P8 and P9 refer to the recording mode of P0;
taking the mean value of all the test values as the offset and the variance of all the test values as the amplitude to generate a Gaussian distribution function with the test values as variables and the probability as the output value
Wherein mu is the mean of all test values, and sigma is the variance of all test values;
the probability distribution is obtained from each test value of 10 AEP memories, and the symmetry axis O, the first turning point M and the second turning point N are taken as a grading basis and can be divided into four grades, as shown in fig. 5, the right side of the second turning point N is a first grade, the right side of the second turning point N is a second grade from the second turning point N to the symmetry axis O, the symmetry axis 0 to the first turning point M is a third grade, and the left side of the first turning point M is a fourth grade;
comparing the number of the grades to sort the 10 AEPs, respectively assigning weights to the four grades, further performing hierarchical comparison, wherein the first-grade weight is assigned to 40%, the second-grade weight is assigned to 30%, the third-grade weight is assigned to 20%, the fourth-grade weight is assigned to 10%, and the total value is compared, namely an evaluation index;
for example, there are 10 parameters in P0 at the first level, 9 parameters at the second level, 0 parameters at the third level, and 20 parameters at the fourth level; there are 0 parameters in P1 at the first level, 20 parameters at the second level, 19 parameters at the third level, and 0 parameters at the fourth level;
calculating P0-10 x0.4+9x0.3+0x0.2+20x 0.1-8.7;
P1＝0x0.4+20x0.3+19x0.2+0x0.1＝9.8；
since P1 is greater than P0, P1 is preferred over P0.
Example 5:
as shown in fig. 4, the present invention provides an apparatus for performing a hierarchical evaluation on a PMEM memory, including:
the test item layering module 1 is used for layering the test items of the PMEM memory according to the working mode;
the layered test module 2 is used for acquiring a PMEM memory sample set, testing according to layered test items and recording layered test results;
a probability distribution function calculation module 3, configured to calculate a probability distribution function of a test result in the PMEM memory sample set;
the evaluation index calculation module 4 is used for dividing the level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index;
and the PMEM memory grading evaluation module 5 is used for grading and evaluating each PMEM memory according to the evaluation index.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for carrying out grading evaluation on a PMEM memory is characterized by comprising the following steps:
s1, layering test items of the PMEM memory according to a working mode;
s2, acquiring a PMEM memory sample set, testing according to layered test items, and recording layered test results;
s3, calculating a probability distribution function of a test result in the PMEM memory sample set;
s4, dividing level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index;
and S5, carrying out grading evaluation on each PMEM memory according to the evaluation indexes.
2. The method for hierarchical evaluation of PMEM memory as claimed in claim 1 wherein step S1 specifically comprises the steps of:
s11, acquiring a large class to which the PMEM memory belongs;
s12, obtaining a test mode of each main type of the PMEM memory;
s13, obtaining a test sub item of each test mode under each major category of the PMEM;
and S14, layering all the test sub-items of the PMEM according to the large class and the test mode.
3. The method for hierarchical evaluation of PMEM memory as claimed in claim 2 wherein S2 comprises the following steps:
s21, acquiring a PMEM memory sample set;
s22, testing each testing sub item of each PMEM memory in the PMEM memory sample set for a preset number of times, and recording each testing result;
s23, judging the test of each test sub-item, and judging whether the test meets the Bernoulli distribution function;
if yes, go to step S24;
if not, eliminating the PMEM memory corresponding to the test sub-item;
and S24, removing the maximum value and the minimum value of all the test results of each test sub-item, and then taking the average value as the test result of the corresponding test sub-item.
4. The method for hierarchical evaluation of PMEM memory as claimed in claim 3, wherein step S3 specifically comprises the steps of:
s31, counting the test result of the test sub-item of each PMEM memory in the PMEM memory sample set, and setting the test result as a test value;
s32, calculating the mean value and the variance of all test values in the PMEM memory sample set;
and S33, generating a Gaussian distribution function which takes the test values as variables and the probabilities as output values by taking the mean value of all the test values as offset and the variance of all the test values as amplitude.
5. The method for hierarchical evaluation of PMEM memory as claimed in claim 4 wherein step S4 specifically comprises the steps of:
s41, dividing the Gaussian distribution function into four intervals according to a curve symmetry axis and a turning point;
s42, dividing four intervals into four levels according to the variable size, setting weight for each level, and setting the interval weight with a large variable value to be larger than the interval weight with a small variable value;
s43, counting the number of the test values in each level interval in each PMEM, and carrying out weighted summation according to set weights to obtain the evaluation index of each PMEM.
6. The method for hierarchical evaluation of PMEM memory as claimed in claim 5, wherein step S5 specifically comprises the steps of:
s51, sequencing the PMEM memories in the PMEM memory sample set according to the evaluation indexes;
and S52, the performance of the PMEM memory with large evaluation index value is higher than that of the PMEM memory with low evaluation index value.
7. The method for hierarchical evaluation of PMEM memory as claimed in claim 5, wherein step S5 specifically comprises the steps of:
S51A, creating a PMEM memory classification algorithm model;
S52A, obtaining evaluation indexes and actual index expressions of PMEM memories in a PMEM memory sample set;
S53A, training a PMEM memory classification algorithm model through evaluation indexes and actual index expressions of each PMEM memory;
S54A, performing prediction grading evaluation on the PMEM memory in the new PMEM memory sample set by using a PMEM memory classification algorithm model to obtain a classification result;
and S55A, acquiring time index performance of the PMEM memory in a new PMEM memory sample set, comparing the time index performance with a classification result of prediction grading evaluation, and correcting a PMEM memory classification algorithm model.
8. The method for hierarchical evaluation of PMEM memory as claimed in claim 7 wherein the PMEM memory classification algorithm model employs one or more of bayesian algorithm, random forest algorithm or mean algorithm.
9. The method according to claim 7, wherein the classification result parameters are set in the PMEM memory classification algorithm model;
when the number of samples in the PMEM memory sample set is smaller than a set threshold, setting the weight of the classification result parameter to be 0;
when the sum of the number of the samples in the PMEM memory sample set and the number of the samples in the new PMEM memory sample set reaches a set threshold value, increasing the weight of classification result parameters;
and when the test requirement is to perform grading evaluation on a certain function or a certain mode of the PMEM memory, setting the weight of the parameter corresponding to the function or the mode which is not to be tested in the PMEM memory classification algorithm model to be 0.
10. An apparatus for performing a hierarchical evaluation of PMEM memory, comprising:
the test item layering module (1) is used for layering the test items of the PMEM memory according to the working mode;
the layered test module (2) is used for acquiring a PMEM memory sample set, testing according to layered test items and recording layered test results;
a probability distribution function calculation module (3) for calculating the probability distribution function of the test result in the PMEM memory sample set;
the evaluation index calculation module (4) is used for dividing the level intervals on the probability distribution function, setting a weight for each level interval, counting the number of test results in each level interval, and performing weighted summation on each PMEM memory to obtain an evaluation index;
and the PMEM memory grading evaluation module (5) is used for grading and evaluating each PMEM memory according to the evaluation index.
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