In addition, not all computational tasks lend themselves to efficient parallelization. Already in 1967, Gene Amdahl presented a calculation of the speedup in latency, now known in computer science as Amdahl’s Law. Amdahl’s Law takes into consideration the computational time of those portions of a task that cannot be parallelized. For example, a computer is tasked with processing 1,000 data items; a naïve assumption might lead one to believe that 10 processors can process 100 data items each, thus reducing the task computing time by 90%. However, the subtask of initially reading the data cannot always be parallelized. The subtask of assembling and displaying the results of the computation might not be suitable for parallelization either. Consequently, only a fraction (albeit a large one) of the task can be distributed across multiple processors.
When taking into account the portions of the task that benefit from parallelization and those that do not, Amdahl’s Law proposes the following terminology and relationship:
- Slatency: the theoretical speedup of the execution of the whole task
- s: the speedup of the part of the task that benefits from increased number of processors and memory
- p: the proportion of execution time of the whole task concerning the parallelizable part of the task before parallelization.
Given the above terminology, Amdahl’s Law defines the following relationship:
Since Slatency 1/(1 – p), it shows that a small part of the program which cannot be parallelized will limit the overall speedup available from parallelization.
Computer science is a fascinating field. As a computer science professional, one has the opportunities to invent, discover, make history and affect the daily lives of billions. All that is needed is an appreciation for the beauty of mathematics, the perseverance to spend days and nights solving difficult problems and a keen affinity for the scientific mindset. Will you join us in building the next Raspberry Pi cluster with 256 CPU? What about one with 8 GPU (graphic cards)? Would you like to multiply 1,000,000 numbers in microseconds? How difficult is to search and detect a face in a crowd of 100,000 fans attending a football game? What should be the initial trajectory of a rocket bound to intercept an asteroid? Can your self-driving car distinguish between two motorcycles and an incoming truck at night before attempting to drive through?
Computer science education can provide you with the knowledge, tools and experience to collaborate with scientists and engineers who tackle all of these fascinating problems.
To learn more about opportunities available with Grand Canyon University’s College of Science, Engineering and Technology, visit our website or click the Request More Information button on this page.
