Description
Case Study 1: The Ideal HPC Programming Language
Read the article titled, “The Ideal HPC Programming Language” dated 2010, located here.
Article Source: Loh, E. (2010, June). The ideal hpc programming language. Queue, 8(6), 30.
Write a two to three (2-3) page paper in which you:
- Summarize the main points of the article.
- Describe one (1) way in which arrays are used in high-performance computing.
- Explain one (1) new concept that you have learned about arrays and one (1) new concept that you have learned about loops as a result of reading the assigned article.
Your assignment must follow these formatting requirements:
- Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
- Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
- Distinguish among the basic types, steps, and properties of programming.
- Describe the use of arrays and subscripts, and the steps involved in declaring, initializing, loading, and searching arrays.
- Use technology and information resources to research issues in computer programming design.
- Write clearly and concisely about computer programming design topics using proper writing mechanics and technical style convention.
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CUNY John Jay College Deletion Using Bplus Tree Java Program
Appendix B. Pseudo-code for Inserting an Entry in a B+-tree Algorithm 1: insert(key K, pointer P) 1 if tree is empty then ...
CUNY John Jay College Deletion Using Bplus Tree Java Program
Appendix B. Pseudo-code for Inserting an Entry in a B+-tree Algorithm 1: insert(key K, pointer P) 1 if tree is empty then 2 Create an empty leaf node L 3 insert in leaf(L, K, P) 4 Register node L as the new root 5 else 6 Find the leaf node L that should contain key K 7 if L has less than n − 1 keys then 8 insert in leaf(L, K, P) 9 else 10 Copy L.P0 · · ·L.Kn−2 to a block of memory T that can hold n (pointer, key) pairs 11 insert in leaf(T, K, P) 12 Create node L 0 13 Set L 0 .Pn−1 = L.Pn−1 14 Erase L.P0 through L.Kn−2 from L 15 Copy T.P0 through T.Kdn/2e−1 from T into L starting at L.P0 16 Copy T.Pdn/2e through T.Kn−1 from T into L 0 starting at L 0 .P0 17 Set L.Pn−1 = L 0 18 Let K0 be the smallest key in L 0 19 insert in parent(L, K0 , L 0 ) Algorithm 2: insert in leaf(node L, key K, pointer P) 1 if K < L.K0 then 2 Insert P, K into L just before L.P0 3 else 4 Let Ki be the highest key in L that is less than K 5 Insert P, K into L just after T.Ki 3 Algorithm 3: insert in parent(node N, key K, node N0 ) 1 if N is the root of the tree then 2 Create a new node R containing N, K, N0 3 Register node R as the new root of the tree 4 return 5 Let P = parent(N) 6 if P has less than n pointers then 7 Insert(K, N0 ) in P just after N 8 else 9 Copy P to a block of memory T that can hold P and (K, N0 ) 10 Insert (K, N0 ) into T just after N 11 Erase all entries from P 12 Create node P 0 13 Copy T.P0 · · · T.Pd(n+1)/2e−1 into P 14 Copy T.Pd(n+1)/2e · · · T.Pn into P 0 15 Let K0 = T.Kd(n+1)/2e−1 16 insert in parent(P, K0 , P 0 ) Appendix C. Pseudo-code for Deleting an Entry in a B+-tree Algorithm 4: delete(key K) 1 find the leaf node L that contains K 2 delete entry(L, K) 4 Algorithm 5: delete entry(node N, key K) 1 delete K and the corresponding pointer from N 2 if N is the root then 3 if N has only one remaining child then 4 Register the child of N as the new root of the tree 5 else if N has too few keys/pointers then 6 Let N0 be the previous or next child of parent(N) 7 Let K0 be the key between pointers N and N0 in parent(N) 8 if entries in N and N0 can fit in a single node then 9 if N0 is the predecessor of N then 10 merge(N0 , K0 , N) 11 else 12 merge(N, K0 , N0 ) 13 else 14 // redistribution: move a key and a pointer from node N0 to node N 15 if N0 is the predecessor of N then 16 if N is a non-leaf node then 17 Let m be such that N0 .Pm is the last pointer in N0 18 Insert (N0 .Pm, K0 ) as the first pointer and key in N by shifting other pointers and keys right 19 Remove (N0 .Km−1, N0 .Pm) from N0 20 Replace K0 in parent(N) by N0 .Km−1 21 else 22 Let m be such that (N0 .Pm, N0 .Km) is the last pointer/key pair in N0 23 Insert (N0 .Pm, N0 .Km) as the first pointer and key in N by shifting other pointers and keys right 24 Remove (N0 .Pm, N0 .Km) from N0 25 Replace K0 in parent(N) by N0 .Km 26 else 27 . . . symmetric to the then case . . . Algorithm 6: merge(node N0 , key K0 , node N) 1 if N is not a leaf node then 2 Append K0 and all pointers and keys in N to N0 3 else 4 Append all (Ki , Pi) pairs in N to N0 5 Set N0 .Pn−1 = N.Pn−1 6 delete entry(parent(N), K0 ) Appendix D. Execution Log When you complete this assignment, your program will produce the following output: input.txt % insert c 1 @0(1, c, null, null, null) % insert d 2 5 @0(1, c, 2, d, null) % insert f 3 @0(@1, f, @2, null, null) @1(1, c, 2, d, @2) @2(3, f, null, null, null) % insert a 5 @0(@1, d, @2, f, @3) @1(5, a, 1, c, @2) @2(2, d, null, null, @3) @3(3, f, null, null, null) % insert b 6 @0(@1, d, @2, null, null) @1(@3, c, @4, null, null) @3(5, a, 6, b, @4) @4(1, c, null, null, @5) @2(@5, f, @6, null, null) @5(2, d, null, null, @6) @6(3, f, null, null, null) % insert b 7 InvalidInsertionException @0(@1, d, @2, null, null) @1(@3, c, @4, null, null) @3(5, a, 6, b, @4) @4(1, c, null, null, @5) @2(@5, f, @6, null, null) @5(2, d, null, null, @6) @6(3, f, null, null, null) % delete d @0(@1, c, @2, d, @3) @1(5, a, 6, b, @2) @2(1, c, null, null, @3) @3(3, f, null, null, null) % delete c @0(@1, d, @2, null, null) @1(5, a, 6, b, @2) @2(3, f, null, null, null) % delete c InvalidDeletionException @0(@1, d, @2, null, null) @1(5, a, 6, b, @2) @2(3, f, null, null, null) % delete f @0(5, a, 6, b, null)
predictive analytics techniques and graphs
The purpose of this assignment is to use predictive analytics
techniques and the graphs and charts associated with thes ...
predictive analytics techniques and graphs
The purpose of this assignment is to use predictive analytics
techniques and the graphs and charts associated with these techniques
to forecast outcomes and make business decisions.Using specified data files, chapter example files, and templates
from the “Topic 1 Student Data, Template, and Example
Files” topic material, complete Chapter 12 Problems 28, 30, 34,
46, 60, 62 (b through e), and 63 from the textbook. Use the Palisade
DecisionTools Excel software to complete these problems where
requested and applicable. Problem 60 should be completed using only Excel.To receive full credit on the assignment, complete the following.Ensure that the Palisade software output is included with your
submission.Ensure that Excel files include the associated
cell functions and/or formulas if functions and/or formulas are
used.Include a written response to all narrative questions
presented in the problem by placing it in the associated Excel
file.Place each problem in its own Excel file. Ensure that
your first and last name are in your Excel file names.APA style is not required, but solid academic writing is expected.This assignment uses a rubric. Please review the rubric prior to
beginning the assignment to become familiar with the expectations for
successful completion. 28.The
file P12_10.xlsx contains annual revenues for a convenience store. If
you want to forecast revenue for the next few years with the moving
averages method, what span should you use? Will any span work well?.
30.The
file P02_28.xlsx contains total monthly U.S. retail sales data. While
holding out the final six months of observations for validation
purposes, use the method of moving averages with one or more spans of
your choice to forecast U.S. retail sales for the next 12 months.
Comment on the performance of your model. What makes this time series
more challenging to forecast?
34.Consider
the American Express closing price data in the file
P12_16.xlsx.a.Create a time series chart of the data. Based on what you
see, which of the exponential smoothing models do you think should be
used for forecasting? Why?b.Use Holt’s exponential smoothing to forecast
these data, using no holdout period and requesting 20days of future
forecasts. Use the default smoothing constants of 0.1.c.Repeat part b,
optimizing the smoothing constants. Does it make much of an
improvement?d.Repeat parts a and b, this time using a holdout period of
50 days.e.Write a short report to summarize your results.
46.The
file P12_46.xlsx contains monthly time series data for total U.S.
retail sales of building materials, garden equipment, and supplies
dealers.a.Is seasonality present in these data? If so, characterize the
seasonality pattern.b.Use the Deseasonalize option in StatTools to
forecast the deseasonalized data for each month of the next year using
the moving average method with an appropriate span.c.Does Holt’s
exponential smoothing method, with optimal smoothing constants,
outperform the moving average method employed in part b? Demonstrate why
or why not.
60.The
file P12_60.xlsx lists annual revenues (in mil-lions of dollars) for
Nike. Create a time series graph of these data. Then superimpose a trend
line with Excel’s Trendline option. Which of the possible Trendline
options seems to provide the best fit? Using this option, what are your
forecasts for the next two years?
62.The file P12_62.xlsx contains data on a motel chain’s revenue and advertising.
b.Use
simple exponential smoothing to make predictions for the motel chain’s
revenues during the next four quarters.c.Use Holt’s method to make
forecasts for the motel chain’s revenues during the next four
quarters.d.Use Winters’ method to determine predictions for the motel
chain’s revenues during the next four quarters.
e.Which of these forecasting methods would you expect to be the most accurate for these data?
63.
The file P12_63.xlsx contains data on monthly U.S. permits for new
housing units (in thousands of houses).a.Using Winters’ method, find
values of α, β, and γthat yield an RMSE as small as possible. Does this
method track the housing crash in recent years?b.Although we have not
discussed autocorrelation for smoothing methods, good forecasts derived
from smoothing methods should exhibit no substantial autocorrelation in
their forecast errors. Is this true for the forecasts in part a?c.At the
end of the observed period, what is the forecast of housing sales
during the next few months?
THE QUESTIONS NEED TO BE ...Palisade outputs are all complete and correct, Functions and formulas are complete and accurate, Narrative answers to problems are flawless with extensive supporting details.
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You have gathered client and user requirements for the new spa website.
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15 pages
619702 Xls Eng
This courseware was prepared solely as the basis for class discussion. Copyright © 2018, 2020 President and Fellows of Ha ...
619702 Xls Eng
This courseware was prepared solely as the basis for class discussion. Copyright © 2018, 2020 President and Fellows of Harvard College. To order ...
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CUNY John Jay College Deletion Using Bplus Tree Java Program
Appendix B. Pseudo-code for Inserting an Entry in a B+-tree Algorithm 1: insert(key K, pointer P) 1 if tree is empty then ...
CUNY John Jay College Deletion Using Bplus Tree Java Program
Appendix B. Pseudo-code for Inserting an Entry in a B+-tree Algorithm 1: insert(key K, pointer P) 1 if tree is empty then 2 Create an empty leaf node L 3 insert in leaf(L, K, P) 4 Register node L as the new root 5 else 6 Find the leaf node L that should contain key K 7 if L has less than n − 1 keys then 8 insert in leaf(L, K, P) 9 else 10 Copy L.P0 · · ·L.Kn−2 to a block of memory T that can hold n (pointer, key) pairs 11 insert in leaf(T, K, P) 12 Create node L 0 13 Set L 0 .Pn−1 = L.Pn−1 14 Erase L.P0 through L.Kn−2 from L 15 Copy T.P0 through T.Kdn/2e−1 from T into L starting at L.P0 16 Copy T.Pdn/2e through T.Kn−1 from T into L 0 starting at L 0 .P0 17 Set L.Pn−1 = L 0 18 Let K0 be the smallest key in L 0 19 insert in parent(L, K0 , L 0 ) Algorithm 2: insert in leaf(node L, key K, pointer P) 1 if K < L.K0 then 2 Insert P, K into L just before L.P0 3 else 4 Let Ki be the highest key in L that is less than K 5 Insert P, K into L just after T.Ki 3 Algorithm 3: insert in parent(node N, key K, node N0 ) 1 if N is the root of the tree then 2 Create a new node R containing N, K, N0 3 Register node R as the new root of the tree 4 return 5 Let P = parent(N) 6 if P has less than n pointers then 7 Insert(K, N0 ) in P just after N 8 else 9 Copy P to a block of memory T that can hold P and (K, N0 ) 10 Insert (K, N0 ) into T just after N 11 Erase all entries from P 12 Create node P 0 13 Copy T.P0 · · · T.Pd(n+1)/2e−1 into P 14 Copy T.Pd(n+1)/2e · · · T.Pn into P 0 15 Let K0 = T.Kd(n+1)/2e−1 16 insert in parent(P, K0 , P 0 ) Appendix C. Pseudo-code for Deleting an Entry in a B+-tree Algorithm 4: delete(key K) 1 find the leaf node L that contains K 2 delete entry(L, K) 4 Algorithm 5: delete entry(node N, key K) 1 delete K and the corresponding pointer from N 2 if N is the root then 3 if N has only one remaining child then 4 Register the child of N as the new root of the tree 5 else if N has too few keys/pointers then 6 Let N0 be the previous or next child of parent(N) 7 Let K0 be the key between pointers N and N0 in parent(N) 8 if entries in N and N0 can fit in a single node then 9 if N0 is the predecessor of N then 10 merge(N0 , K0 , N) 11 else 12 merge(N, K0 , N0 ) 13 else 14 // redistribution: move a key and a pointer from node N0 to node N 15 if N0 is the predecessor of N then 16 if N is a non-leaf node then 17 Let m be such that N0 .Pm is the last pointer in N0 18 Insert (N0 .Pm, K0 ) as the first pointer and key in N by shifting other pointers and keys right 19 Remove (N0 .Km−1, N0 .Pm) from N0 20 Replace K0 in parent(N) by N0 .Km−1 21 else 22 Let m be such that (N0 .Pm, N0 .Km) is the last pointer/key pair in N0 23 Insert (N0 .Pm, N0 .Km) as the first pointer and key in N by shifting other pointers and keys right 24 Remove (N0 .Pm, N0 .Km) from N0 25 Replace K0 in parent(N) by N0 .Km 26 else 27 . . . symmetric to the then case . . . Algorithm 6: merge(node N0 , key K0 , node N) 1 if N is not a leaf node then 2 Append K0 and all pointers and keys in N to N0 3 else 4 Append all (Ki , Pi) pairs in N to N0 5 Set N0 .Pn−1 = N.Pn−1 6 delete entry(parent(N), K0 ) Appendix D. Execution Log When you complete this assignment, your program will produce the following output: input.txt % insert c 1 @0(1, c, null, null, null) % insert d 2 5 @0(1, c, 2, d, null) % insert f 3 @0(@1, f, @2, null, null) @1(1, c, 2, d, @2) @2(3, f, null, null, null) % insert a 5 @0(@1, d, @2, f, @3) @1(5, a, 1, c, @2) @2(2, d, null, null, @3) @3(3, f, null, null, null) % insert b 6 @0(@1, d, @2, null, null) @1(@3, c, @4, null, null) @3(5, a, 6, b, @4) @4(1, c, null, null, @5) @2(@5, f, @6, null, null) @5(2, d, null, null, @6) @6(3, f, null, null, null) % insert b 7 InvalidInsertionException @0(@1, d, @2, null, null) @1(@3, c, @4, null, null) @3(5, a, 6, b, @4) @4(1, c, null, null, @5) @2(@5, f, @6, null, null) @5(2, d, null, null, @6) @6(3, f, null, null, null) % delete d @0(@1, c, @2, d, @3) @1(5, a, 6, b, @2) @2(1, c, null, null, @3) @3(3, f, null, null, null) % delete c @0(@1, d, @2, null, null) @1(5, a, 6, b, @2) @2(3, f, null, null, null) % delete c InvalidDeletionException @0(@1, d, @2, null, null) @1(5, a, 6, b, @2) @2(3, f, null, null, null) % delete f @0(5, a, 6, b, null)
predictive analytics techniques and graphs
The purpose of this assignment is to use predictive analytics
techniques and the graphs and charts associated with thes ...
predictive analytics techniques and graphs
The purpose of this assignment is to use predictive analytics
techniques and the graphs and charts associated with these techniques
to forecast outcomes and make business decisions.Using specified data files, chapter example files, and templates
from the “Topic 1 Student Data, Template, and Example
Files” topic material, complete Chapter 12 Problems 28, 30, 34,
46, 60, 62 (b through e), and 63 from the textbook. Use the Palisade
DecisionTools Excel software to complete these problems where
requested and applicable. Problem 60 should be completed using only Excel.To receive full credit on the assignment, complete the following.Ensure that the Palisade software output is included with your
submission.Ensure that Excel files include the associated
cell functions and/or formulas if functions and/or formulas are
used.Include a written response to all narrative questions
presented in the problem by placing it in the associated Excel
file.Place each problem in its own Excel file. Ensure that
your first and last name are in your Excel file names.APA style is not required, but solid academic writing is expected.This assignment uses a rubric. Please review the rubric prior to
beginning the assignment to become familiar with the expectations for
successful completion. 28.The
file P12_10.xlsx contains annual revenues for a convenience store. If
you want to forecast revenue for the next few years with the moving
averages method, what span should you use? Will any span work well?.
30.The
file P02_28.xlsx contains total monthly U.S. retail sales data. While
holding out the final six months of observations for validation
purposes, use the method of moving averages with one or more spans of
your choice to forecast U.S. retail sales for the next 12 months.
Comment on the performance of your model. What makes this time series
more challenging to forecast?
34.Consider
the American Express closing price data in the file
P12_16.xlsx.a.Create a time series chart of the data. Based on what you
see, which of the exponential smoothing models do you think should be
used for forecasting? Why?b.Use Holt’s exponential smoothing to forecast
these data, using no holdout period and requesting 20days of future
forecasts. Use the default smoothing constants of 0.1.c.Repeat part b,
optimizing the smoothing constants. Does it make much of an
improvement?d.Repeat parts a and b, this time using a holdout period of
50 days.e.Write a short report to summarize your results.
46.The
file P12_46.xlsx contains monthly time series data for total U.S.
retail sales of building materials, garden equipment, and supplies
dealers.a.Is seasonality present in these data? If so, characterize the
seasonality pattern.b.Use the Deseasonalize option in StatTools to
forecast the deseasonalized data for each month of the next year using
the moving average method with an appropriate span.c.Does Holt’s
exponential smoothing method, with optimal smoothing constants,
outperform the moving average method employed in part b? Demonstrate why
or why not.
60.The
file P12_60.xlsx lists annual revenues (in mil-lions of dollars) for
Nike. Create a time series graph of these data. Then superimpose a trend
line with Excel’s Trendline option. Which of the possible Trendline
options seems to provide the best fit? Using this option, what are your
forecasts for the next two years?
62.The file P12_62.xlsx contains data on a motel chain’s revenue and advertising.
b.Use
simple exponential smoothing to make predictions for the motel chain’s
revenues during the next four quarters.c.Use Holt’s method to make
forecasts for the motel chain’s revenues during the next four
quarters.d.Use Winters’ method to determine predictions for the motel
chain’s revenues during the next four quarters.
e.Which of these forecasting methods would you expect to be the most accurate for these data?
63.
The file P12_63.xlsx contains data on monthly U.S. permits for new
housing units (in thousands of houses).a.Using Winters’ method, find
values of α, β, and γthat yield an RMSE as small as possible. Does this
method track the housing crash in recent years?b.Although we have not
discussed autocorrelation for smoothing methods, good forecasts derived
from smoothing methods should exhibit no substantial autocorrelation in
their forecast errors. Is this true for the forecasts in part a?c.At the
end of the observed period, what is the forecast of housing sales
during the next few months?
THE QUESTIONS NEED TO BE ...Palisade outputs are all complete and correct, Functions and formulas are complete and accurate, Narrative answers to problems are flawless with extensive supporting details.
You have gathered client and user requirements for the new spa website.
You have gathered client and user requirements for the new spa website. You have also developed several design deliverable ...
You have gathered client and user requirements for the new spa website.
You have gathered client and user requirements for the new spa website. You have also developed several design deliverables that you will now resubmit after incorporating your instructor’s feedback. You will now create a high-fidelity prototype using Adobe XD. The prototype must incorporate user-centered design best practices and principles. It should also be visually appealing and easy to use and should conform to the client and user requirements. In addition, you will write a report justifying your choices and providing a plan for usability testing.
15 pages
619702 Xls Eng
This courseware was prepared solely as the basis for class discussion. Copyright © 2018, 2020 President and Fellows of Ha ...
619702 Xls Eng
This courseware was prepared solely as the basis for class discussion. Copyright © 2018, 2020 President and Fellows of Harvard College. To order ...
4 pages
It Support For Virtual Teams Doc1122
Issues that IT Department Could Face When Supporting Virtual Teams The issue of supporting virtual teams requires the IT d ...
It Support For Virtual Teams Doc1122
Issues that IT Department Could Face When Supporting Virtual Teams The issue of supporting virtual teams requires the IT department to focus on ...
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