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(b) Scientific simulations: DLP plays a Multi-core processors
crucial role in scientific simulations The rise of multicore processors has been
that involve complex calculations a major driver for DLP. With multiple
on massive datasets. For example, cores available, data can be efficiently
weather simulations can partition distributed and processed concurrently,
FOR ONLINE READING ONLY
weather data for different geographical leading to significant performance gains Chapter Two: Performance and optimisation of computer processor
regions across multiple cores, for compatible applications. Prior to this,
enabling faster and more accurate processors primarily relied on ILP, which
focused on exploiting parallelism within
modeling. a single instruction stream. However,
(c) Video editing: Video editing software multicore architectures opened the door
often utilizes DLP for tasks like for a more granular approach: dividing
encoding or decoding video frames. the data itself.
By distributing the processing of
individual frames across cores, the The multicore revolution was not the
editing process becomes smoother only factor driving DLP advancements.
and faster. The introduction of specialised
(d) Financial modeling: Complex hardware like Graphics Processing
Units (GPUs) opened new avenues for
financial models involve numerous parallel processing. GPUs often contain
calculations on extensive financial thousands of cores specifically designed
data. DLP allows us to distribute these for highly parallel workloads. This has
calculations across multiple cores, fueled the adoption of DLP techniques
resulting in quicker risk assessments including Single Instruction Multiple
and portfolio optimisations. Data (SIMD), Single Instruction Multiple
Thread (SIMT), Data partitioning, Load

The benefits of DLP extend beyond the Balancing and Functional programming.
given examples. Tasks like scientific
computing, data mining, and machine Data Level Parallellism techniques
learning all rely on efficient data (a) SIMD (Single Instruction Multiple
processing, making DLP a cornerstone Data)
of high-performance computing. Modern A widely used technique where a single
computer systems often utilise parallel instruction operates on multiple data
memory architectures like DDR (Double elements simultaneously. Imagine a group
Data Rate) memory. These systems of chefs preparing identical meals for
allow for reading or writing multiple multiple guests. They all follow the same
data elements simultaneously, further recipe (instruction) but simultaneously
enhancing the effectiveness of data-level operate on individual ingredients (data
elements). This is the essence of SIMD.
parallelism.

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Student’s Book Form Five

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