2. Table of contents:
1. What’s a specialized parallel computer.
2. Reconfigurable computing with field-
programmable gate arrays (FPGA).
3. General-purpose computing on graphics
processing units (GPGPU).
4. Application-specific integrated circuits.
5. Vector processors.
3. What’s a specialized parallel computer?
Within parallel
computing, there are
specialized parallel
devices, that tend to be
applicable to only a few
classes of parallel
problems.
6. Reconfigurable computing:
01
Can be used by implementing all of the
application functionalities in hardware.
The main advantage in this, is that the
hardware can be easily replaced by
downloading a configuration file on the
chip, rather than having the circuit
physically replaced.
7. We can thus conclude that
reconfigurable computing is a
trade off between general
purpose computing and
application specific
computing, because it tries to
achieve balance among
performance, cost, power,
flexibility, and design effort.
8. Is a semiconductor
device containing
programmable logic
components,
programmable
interconnects and
I/O Blocks.
Field programmable gate arrays:
12. Arduino Microcontrollers are not FPGAs
The main difference is that Arduino
executes operations in a sequential
fashion, whereas FPGAs are parallel
computing devices.
15. What is General-purpose graphics
processing units (GPGPU) ?
Is a graphics processing unit (GPU) that is
programmed for purposes beyond graphics
processing.
GPU + General purpose computing
16. Benefit of using GPGPU.
GPUs can run certain algorithms
anywhere from 10 to 100 or more times
faster than CPUs.
17. Uses of GPGPU
● Scientific Computation
● generation of cypto currencies such as Bitcoin.
● Machine learning programs
19. An application-specific
integrated circuit (ASIC
/ˈeɪsɪk/) is an integrated
circuit (IC) chip
customized for a
particular use, rather than
intended for general-
purpose use.
20. For example a chip designed to run in a
digital voice recorder or a high-efficiency
bitcoin miner is an ASIC. Application-
specific standard product (ASSP) chips
are intermediate between ASICs and
industry standard integrated circuits like
the 7400 series or the 4000 series. ASIC
chips are typically fabricated using
metal-oxide-semiconductor (MOS)
technology, as MOS integrated circuit
chips.
21. Early ASICs used gate array technology. By 1967, Ferranti and Interdesign were
manufacturing early bipolar gate arrays. In 1967, Fairchild Semiconductor
introduced the Micromatrix family of bipolar diode–transistor logic (DTL) and
transistor–transistor logic (TTL) arrays.
History
22. Gate array design is a
manufacturing method
in which diffused layers,
each consisting of
transistors and other
active devices
Gate-array
23. By contrast, full-custom ASIC
design defines all the
photolithographic layers of
the device. Full-custom design
is used for both ASIC design
and for standard product
design.
Full-custom design
25. What does Vector Processor mean?
A vector processor is a central processing unit that can work on an
entire vector in one instruction. The instruction to the processor is in the
form of one complete vector instead of its element.
Vector processors are used because they reduce the draw and interpret
bandwidth owing to the fact that fewer instructions must be fetched.
A vector processor is also known as an array processor.
26.
27. Advantages of Vector Processor
● Vector processor uses vector instructions by
which code density of the instructions can be
improved.
● The sequential arrangement of data helps to
handle the data by the hardware in a better way.
● It offers a reduction in instruction bandwidth
A general-purpose computer is one that, given the appropriate application and required time, should be able to perform most common computing tasks.
such as performing computations typically conducted by a Central Processing Unit (CPU).
Essentially all modern GPUs are GPGPU.
. While GPUs were originally designed primarily for the purpose of rendering images, GPGPUs can now be programmed to direct that processing power toward addressing scientific computing needs as well.
GPUs can process far more pictures and graphical data per second than a traditional CPU. Migrating data into graphical form and then using the GPU to scan and analyze it can create a large speedup.