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The dramatic increase in computer performance has been
extraordinary, but not for all computations: it has key limits and
structure. Software architects, developers, and even data
scientists need to understand how exploit the fundamental structure
of computer performance to harness it for future applications.
Ideal for upper level undergraduates, Computer Architecture for
Scientists covers four key pillars of computer performance and
imparts a high-level basis for reasoning with and understanding
these concepts: Small is fast - how size scaling drives
performance; Implicit parallelism - how a sequential program can be
executed faster with parallelism; Dynamic locality - skirting
physical limits, by arranging data in a smaller space; Parallelism
- increasing performance with teams of workers. These principles
and models provide approachable high-level insights and
quantitative modelling without distracting low-level detail.
Finally, the text covers the GPU and machine-learning accelerators
that have become increasingly important for mainstream
applications.
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