Suppose you have a C++ program called prog.cpp. To compile t…
Suppose you have a C++ program called prog.cpp. To compile the program on a Unix system, you would use the command g++ prog.cpp This generates an executable file called . You can then run the program by typing . If you want to specify the name of the output file, you can use the -o flag, as follows: g++ prog.cpp -o prog This will produce an executable file called For programs designed to run in parallel, you might include libraries such as for shared-memory parallelism or for parallelism. To compile a parallel program using OpenMP, you would use the `-fopenmp` flag and run the executable just like a serial program. For MPI-based programs, you compile using a specialized compiler wrapper, such as , and run the program across multiple processes using the command.
Read DetailsDesign a Merkle Tree for the data blocks D1, D2, D3, D4, D5,…
Design a Merkle Tree for the data blocks D1, D2, D3, D4, D5, D6, D7, D8. What is the Merkle Root if the hash function H produces the following hash values for each data block: H(D1) =A1, H(D2) = A2, H(D3) = A3, H(D4) = A4, H(D5) = A5, H(D6) = A6, H(D7) = A7, H(D8) = A8?
Read DetailsBob wants to store a piece of data DDD on the blockchain, en…
Bob wants to store a piece of data DDD on the blockchain, ensuring its integrity. To do so, he generates a hash H(D)H(D), which is the cryptographic hash of the data DD. The hash function HH satisfies the following properties: Preimage Resistance: Given a hash hh, it is computationally infeasible to find DD such that H(D)=hH(D) = h. Collision Resistance: It is computationally infeasible to find two different data sets D1≠D2D_1 \neq D_2 such that H(D1)=H(D2)H(D_1) = H(D_2). Now, consider that Bob stores (D,H(D))(D, H(D)) on the blockchain, and later, Alice retrieves this data. Alice wants to verify the integrity of DD. The integrity check is given by: Integrity Check =True if H(D)==H(D′)Where D′D’ is the retrieved data and H(D′)H(D’) is the hash of the retrieved data. What must Alice check to ensure the integrity of the data?
Read DetailsYou are running a program to calculate an approximation of a…
You are running a program to calculate an approximation of an integral using multiple threads. The program uses a mutex (i.e. #pragma omp critical) to protect access to a shared variable (sum) where each thread accumulates partial results. You observe the following behavior when increasing the number of threads: Performance improves significantly as the number of threads increases, up to the number of available CPU cores. However, beyond the number of available CPU cores, performance remains roughly constant, even as more threads are added. Explain why the performance of the program plateaus when the number of threads exceeds the number of CPU cores. Describe how the use of mutexes impacts thread performance in this scenario. Suggest one optimization that could reduce the performance impact of using a mutex in this program.
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