The document discusses parallel processing techniques in computer systems including pipelining and vector processing. It describes different types of parallel architectures like SISD, SIMD, MISD, and MIMD systems. Specific examples of parallel techniques discussed include arithmetic pipelining, instruction pipelining, vector processors, and array processors. The key benefits of these techniques are exploiting parallelism at different levels to improve computational speed and overcome limitations of conventional von Neumann architectures.
This document discusses parallel processing and various techniques used to achieve it, including pipelining and vector processing. It describes different classifications of parallel computers based on the number of instruction and data streams. It also explains single instruction single data (SISD), multiple instruction single data (MISD), single instruction multiple data (SIMD), and multiple instruction multiple data (MIMD) computer architectures. The document further discusses pipelining techniques used to improve performance in SISD systems, and provides details about arithmetic, instruction, and RISC pipelines. It also covers vector processing techniques used in SIMD systems like array processors and systolic arrays.
The document discusses parallel processing and pipelining techniques in computer organization. It covers topics like parallel processing concepts and classifications, pipelining concepts and how it increases computational speed, arithmetic and instruction pipelining, handling pipeline hazards like data dependencies and branches. The key advantages of pipelining include decomposing tasks into sequential sub-operations that can complete concurrently, improving throughput and achieving speedup close to the number of pipeline stages when the number of tasks is large.
This document discusses parallel processing and pipelining. It describes different levels and types of parallel processing including job level, task level, inter-instruction level, and intra-instruction level parallelism. It also covers Flynn's classification of parallel computers as SISD, SIMD, MISD, and MIMD based on the number of instruction and data streams. Pipelining is defined as decomposing a process into sub-operations that execute concurrently. The key benefits of pipelining are that multiple computations can progress simultaneously through different pipeline stages.
The document discusses parallel processing and provides classifications of parallel computer architectures. It describes Flynn's classification of computer architectures as single instruction stream single data stream (SISD), single instruction stream multiple data stream (SIMD), multiple instruction stream single data stream (MISD), and multiple instruction stream multiple data stream (MIMD). It also discusses pipeline computers, array processors, and multiprocessor systems as different architectural configurations for parallel computers. Pipelining is described as a technique to decompose a process into sub-operations that execute concurrently in dedicated segments to achieve overlapping computation.
Pipelining is a technique used in computer processors to overlap the execution of instructions to enhance performance. It works by dividing instruction execution into discrete stages, such as fetch, decode, execute, memory, and write-back, so that multiple instructions can be in different stages at the same time. In a pipelined processor, the average time to complete an instruction is reduced compared to a non-pipelined processor, leading to higher throughput. However, special techniques are needed to handle data and structural hazards that can occur when instructions interact in unexpected ways within the pipeline.
This document discusses parallel processing and pipelining techniques used to improve computer performance. It covers parallel processing classifications including SISD, SIMD, MISD, and MIMD models. Pipelining is defined as decomposing tasks into sequential suboperations that execute concurrently. Arithmetic and instruction pipelines are described as having multiple stages to overlap processing of different instructions. Vector processing and array processors are mentioned as techniques to perform simultaneous operations on multiple data items.
Pipelining of Processors Computer ArchitectureHaris456
Pipelining is a technique used in microprocessors to overlap the execution of multiple instructions to increase throughput. It works by dividing the instruction execution process into discrete stages, such as fetch, decode, execute, memory, and write-back. When an instruction enters one stage, the previous instruction can enter the next stage, allowing the processor to complete more than one instruction per clock cycle. Pipelining reduces the time needed to complete a series of instructions by allowing the stages to process separate instructions simultaneously rather than sequentially.
This document discusses parallel processing and various techniques used to achieve it, including pipelining and vector processing. It describes different classifications of parallel computers based on the number of instruction and data streams. It also explains single instruction single data (SISD), multiple instruction single data (MISD), single instruction multiple data (SIMD), and multiple instruction multiple data (MIMD) computer architectures. The document further discusses pipelining techniques used to improve performance in SISD systems, and provides details about arithmetic, instruction, and RISC pipelines. It also covers vector processing techniques used in SIMD systems like array processors and systolic arrays.
The document discusses parallel processing and pipelining techniques in computer organization. It covers topics like parallel processing concepts and classifications, pipelining concepts and how it increases computational speed, arithmetic and instruction pipelining, handling pipeline hazards like data dependencies and branches. The key advantages of pipelining include decomposing tasks into sequential sub-operations that can complete concurrently, improving throughput and achieving speedup close to the number of pipeline stages when the number of tasks is large.
This document discusses parallel processing and pipelining. It describes different levels and types of parallel processing including job level, task level, inter-instruction level, and intra-instruction level parallelism. It also covers Flynn's classification of parallel computers as SISD, SIMD, MISD, and MIMD based on the number of instruction and data streams. Pipelining is defined as decomposing a process into sub-operations that execute concurrently. The key benefits of pipelining are that multiple computations can progress simultaneously through different pipeline stages.
The document discusses parallel processing and provides classifications of parallel computer architectures. It describes Flynn's classification of computer architectures as single instruction stream single data stream (SISD), single instruction stream multiple data stream (SIMD), multiple instruction stream single data stream (MISD), and multiple instruction stream multiple data stream (MIMD). It also discusses pipeline computers, array processors, and multiprocessor systems as different architectural configurations for parallel computers. Pipelining is described as a technique to decompose a process into sub-operations that execute concurrently in dedicated segments to achieve overlapping computation.
Pipelining is a technique used in computer processors to overlap the execution of instructions to enhance performance. It works by dividing instruction execution into discrete stages, such as fetch, decode, execute, memory, and write-back, so that multiple instructions can be in different stages at the same time. In a pipelined processor, the average time to complete an instruction is reduced compared to a non-pipelined processor, leading to higher throughput. However, special techniques are needed to handle data and structural hazards that can occur when instructions interact in unexpected ways within the pipeline.
This document discusses parallel processing and pipelining techniques used to improve computer performance. It covers parallel processing classifications including SISD, SIMD, MISD, and MIMD models. Pipelining is defined as decomposing tasks into sequential suboperations that execute concurrently. Arithmetic and instruction pipelines are described as having multiple stages to overlap processing of different instructions. Vector processing and array processors are mentioned as techniques to perform simultaneous operations on multiple data items.
Pipelining of Processors Computer ArchitectureHaris456
Pipelining is a technique used in microprocessors to overlap the execution of multiple instructions to increase throughput. It works by dividing the instruction execution process into discrete stages, such as fetch, decode, execute, memory, and write-back. When an instruction enters one stage, the previous instruction can enter the next stage, allowing the processor to complete more than one instruction per clock cycle. Pipelining reduces the time needed to complete a series of instructions by allowing the stages to process separate instructions simultaneously rather than sequentially.
Here are the answers to the questions:
1. Pipeline cycle time = Maximum delay of any stage + Latch delay
= 90 ns + 10 ns = 100 ns
2. Non-pipeline execution time for one task = Total delay of all stages
= 60 + 50 + 90 + 80 = 280 ns
3. Speed up ratio = Non-pipeline time/Pipeline time
= 280/100 = 2.8
4. Pipeline time for 1000 tasks = Pipeline cycle time x Number of tasks
= 100 ns x 1000 = 100,000 ns = 100 μs
5. Sequential time for 1000 tasks = Non-pipeline time per task x Number of tasks
= 280 ns x 1000 = 280,
This document discusses datapath design and arithmetic operations in computer architecture. It covers:
1) The design of circuits to implement basic fixed-point arithmetic instructions like addition, subtraction, multiplication, and division. Multiplication can be done with combinational or sequential circuits using an array of adders, while division is typically sequential using repeated subtraction.
2) The Arithmetic Logic Unit (ALU) is used to process arithmetic and logical instructions and employs a chain of identical 1-bit adders. Coprocessors can provide fast hardware implementations for complex arithmetic functions.
3) Pipeline processing is used to improve processor throughput by dividing arithmetic operations into stages to allow overlapped processing, at the cost of requiring more hardware resources
Design pipeline architecture for various stage pipelinesMahmudul Hasan
This document discusses the concepts of single-cycle control, multi-cycle control, and pipelining in processors. It explains that single-cycle control has a low CPI but a long clock period, while multi-cycle control has a short clock period but high CPI. Pipelining allows overlapping the execution of instructions to improve throughput. The document presents diagrams of 5-stage instruction pipelines and describes the fetch, decode, execute, memory, and write-back stages. It also discusses pipeline hazards and performance improvements from pipelining over single-cycle and multi-cycle designs.
The document discusses pipelining in computer processors. It describes how pipelining can increase throughput by overlapping the execution of multiple instructions. It discusses the basic pipeline stages for a RISC instruction set, including fetch, decode, execute, memory access, and writeback. It also describes several types of pipeline hazards that can occur, such as structural hazards caused by resource conflicts, data hazards when instructions depend on previous results, and control hazards with branches. Forwarding techniques are presented to help address data hazards.
The CPU is the central processing unit of a computer and consists of three main parts - the control unit, register set, and ALU. The control unit directs operations between the register set and ALU. The register set stores intermediate data and the ALU performs arithmetic and logic operations. The CPU follows a fetch-execute cycle where it fetches instructions from memory and stores them in the instruction register before executing them. Common instruction types include processor-memory operations, I/O operations, data processing, and control operations.
This document discusses the implementation of a basic MIPS processor including building the datapath, control implementation, pipelining, and handling hazards. It describes the MIPS instruction set and 5-stage pipeline. The datapath is built from components like registers, ALUs, and adders. Control signals are designed for different instructions. Pipelining is implemented using techniques like forwarding and branch prediction to handle data and control hazards between stages. Exceptions are handled using status registers or vectored interrupts.
Parallel processing involves performing multiple tasks simultaneously to increase computational speed. It can be achieved through pipelining, where instructions are overlapped in execution, or vector/array processors where the same operation is performed on multiple data elements at once. The main types are SIMD (single instruction multiple data) and MIMD (multiple instruction multiple data). Pipelining provides higher throughput by keeping the pipeline full but requires handling dependencies between instructions to avoid hazards slowing things down.
This document discusses parallel processing and different types of parallel computers. It describes Flynn's classification of parallel computers based on the number of instruction and data streams as SISD, SIMD, MISD, and MIMD. It then provides details about each classification including characteristics, examples, and limitations. The document also covers topics like pipelining, interconnection networks, and how pipelining can improve the speed of computation.
The document provides an overview of pipelining in computer processors. It discusses how pipelining works by dividing processor operations like fetch, decode, execute, memory, and write-back into discrete stages that can overlap, improving throughput. Key points made include:
- Pipelining allows multiple instructions to be in different stages of completion at the same time, improving instruction throughput.
- The document uses an example of a sequential laundry process versus a pipelined laundry process to illustrate how pipelining improves efficiency.
- It describes the five main stages of a RISC instruction set pipeline - fetch, decode, execute, memory, and write-back. The work done and data passed between each stage
This document provides an introduction to parallel and pipeline computer processors. It begins by classifying parallel processors according to Flynn's taxonomy, which considers the number of instruction and data streams that can be processed simultaneously. The main categories are single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD). Pipeline processing is also introduced as a form of parallelism. Computational models for parallel processing including speedup and Amdahl's law are discussed. Finally, instruction pipelining is explained through an example showing how nine instructions can be processed in 14 time units using a six-stage pipeline instead of 54 time units sequentially.
This document discusses instruction pipelining and main memory. It begins by explaining how an instruction pipeline works, overlapping the fetch, decode, and execute phases of instruction processing. It notes some difficulties in pipelining including resource conflicts, data dependencies, and branch instructions. It then discusses pipeline control and performance, noting that pipelining provides faster processing by decomposing tasks into sequential sub-operations that can overlap. It concludes by answering questions about pipelining hazards and calculating pipeline metrics for example processors.
This document discusses general-purpose processors. It begins by introducing general-purpose processors and their basic architecture, which consists of a control unit and datapath that is designed to perform a variety of computation tasks. It then describes the operations of loading, storing, and arithmetic/logical operations that can be performed by the datapath. Subsequent sections provide more details on the control unit and how it sequences operations, instruction cycles, architectural considerations like bit-width and clock frequency, and techniques for improving performance like pipelining and superscalar execution. The document concludes with sections on assembly-level instructions and programmer considerations.
This document discusses parallel processing techniques such as pipelining and vector processing to increase computational speed. It covers Flynn's classification of computer architectures, arithmetic pipelining using a floating-point adder as an example, instruction pipelining with a four-segment model, resolving data dependencies and branch difficulties in pipelines, and RISC pipeline examples addressing delayed load and branch issues. The key techniques discussed are decomposing operations into parallel suboperations, hardware interlocks, operand forwarding, and compiler assistance.
The document discusses the basics of RISC instruction set architectures and pipelining in CPUs. It begins by describing properties of RISC ISAs, including that operations apply to full registers, only load/store instructions affect memory, and instructions are typically one size. It then describes different types of RISC instructions like ALU, load/store, and branches. The document goes on to explain the implementation of a RISC pipeline in 5 stages and the concept of pipelining to improve CPU performance by overlapping instruction execution. It also discusses potential hazards that can degrade pipeline performance like structural, data, and control hazards.
The document discusses RISC instruction set basics and pipelining concepts. It begins by describing properties of RISC architectures, including that operations apply to full registers and only load/store instructions affect memory. It then describes different types of RISC instructions like ALU, load/store, and branches. The document goes on to explain the implementation of instructions in a MIPS64 pipeline with 5 stages: instruction fetch, decode/register fetch, execute, memory access, and write-back. It concludes by defining pipelining and describing how it can increase throughput by overlapping instruction execution.
Microchip's PIC Micro Controller - Presentation Covers- Embedded system,Application, Harvard and Von Newman Architecture, PIC Microcontroller Instruction Set, PIC assembly language programming, PIC Basic circuit design and its programming etc.
The document discusses digital signal processors and digital signal controllers. It begins with an overview of microprocessors and microcontrollers before explaining that digital signal processors have additional hardware units that allow them to perform mathematical operations more efficiently compared to general purpose microprocessors. Digital signal controllers combine the computational power of a digital signal processor with integrated memory and peripherals on a single chip for embedded real-time control applications involving intensive math operations. Texas Instruments is a major manufacturer of digital signal processors and produces the C2000 family of digital signal controllers.
The document discusses pipeline computing and its various types and applications. It defines pipeline computing as a technique to decompose a sequential process into parallel sub-processes that can execute concurrently. There are two main types - linear and non-linear pipelines. Linear pipelines use a single reservation table while non-linear pipelines use multiple tables. Common applications of pipeline computing include instruction pipelines in CPUs, graphics pipelines in GPUs, software pipelines using pipes, and HTTP pipelining. The document also discusses implementations of pipeline computing and its advantages like reduced cycle time and increased instruction throughput.
This document summarizes several course projects completed by Setiawan Soekamtoputra for their Master's degree. The projects include:
1) Design of a 32-bit pipelined CPU in Verilog including implementation of an ASIC flow, multiplier with accumulator case study, and pipeline optimization case study.
2) Development of a monitor program for the MC68000 processor in assembly language including common memory and register commands and exception handlers.
3) Implementation of a high-performance pipelined MIPS processor in VHDL including hazard detection and data forwarding units to handle data and branch hazards.
4) Network on chip prototype designs including a 3-node partially connected mesh design in SystemC and
This document discusses pipelining techniques used to improve the performance of arithmetic and instruction processing. It describes how pipelining decomposes operations into sequential sub-operations that can execute concurrently across multiple pipeline stages. In arithmetic pipelining, it provides examples of a floating point adder broken into four stages: compare exponents, align mantissa, add/subtract mantissa, and normalize result. For instruction pipelining, it outlines a four-stage RISC pipeline of fetch, decode/address, fetch operands, and execute stages and how dependencies are handled through techniques like delayed loading and branching.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Here are the answers to the questions:
1. Pipeline cycle time = Maximum delay of any stage + Latch delay
= 90 ns + 10 ns = 100 ns
2. Non-pipeline execution time for one task = Total delay of all stages
= 60 + 50 + 90 + 80 = 280 ns
3. Speed up ratio = Non-pipeline time/Pipeline time
= 280/100 = 2.8
4. Pipeline time for 1000 tasks = Pipeline cycle time x Number of tasks
= 100 ns x 1000 = 100,000 ns = 100 μs
5. Sequential time for 1000 tasks = Non-pipeline time per task x Number of tasks
= 280 ns x 1000 = 280,
This document discusses datapath design and arithmetic operations in computer architecture. It covers:
1) The design of circuits to implement basic fixed-point arithmetic instructions like addition, subtraction, multiplication, and division. Multiplication can be done with combinational or sequential circuits using an array of adders, while division is typically sequential using repeated subtraction.
2) The Arithmetic Logic Unit (ALU) is used to process arithmetic and logical instructions and employs a chain of identical 1-bit adders. Coprocessors can provide fast hardware implementations for complex arithmetic functions.
3) Pipeline processing is used to improve processor throughput by dividing arithmetic operations into stages to allow overlapped processing, at the cost of requiring more hardware resources
Design pipeline architecture for various stage pipelinesMahmudul Hasan
This document discusses the concepts of single-cycle control, multi-cycle control, and pipelining in processors. It explains that single-cycle control has a low CPI but a long clock period, while multi-cycle control has a short clock period but high CPI. Pipelining allows overlapping the execution of instructions to improve throughput. The document presents diagrams of 5-stage instruction pipelines and describes the fetch, decode, execute, memory, and write-back stages. It also discusses pipeline hazards and performance improvements from pipelining over single-cycle and multi-cycle designs.
The document discusses pipelining in computer processors. It describes how pipelining can increase throughput by overlapping the execution of multiple instructions. It discusses the basic pipeline stages for a RISC instruction set, including fetch, decode, execute, memory access, and writeback. It also describes several types of pipeline hazards that can occur, such as structural hazards caused by resource conflicts, data hazards when instructions depend on previous results, and control hazards with branches. Forwarding techniques are presented to help address data hazards.
The CPU is the central processing unit of a computer and consists of three main parts - the control unit, register set, and ALU. The control unit directs operations between the register set and ALU. The register set stores intermediate data and the ALU performs arithmetic and logic operations. The CPU follows a fetch-execute cycle where it fetches instructions from memory and stores them in the instruction register before executing them. Common instruction types include processor-memory operations, I/O operations, data processing, and control operations.
This document discusses the implementation of a basic MIPS processor including building the datapath, control implementation, pipelining, and handling hazards. It describes the MIPS instruction set and 5-stage pipeline. The datapath is built from components like registers, ALUs, and adders. Control signals are designed for different instructions. Pipelining is implemented using techniques like forwarding and branch prediction to handle data and control hazards between stages. Exceptions are handled using status registers or vectored interrupts.
Parallel processing involves performing multiple tasks simultaneously to increase computational speed. It can be achieved through pipelining, where instructions are overlapped in execution, or vector/array processors where the same operation is performed on multiple data elements at once. The main types are SIMD (single instruction multiple data) and MIMD (multiple instruction multiple data). Pipelining provides higher throughput by keeping the pipeline full but requires handling dependencies between instructions to avoid hazards slowing things down.
This document discusses parallel processing and different types of parallel computers. It describes Flynn's classification of parallel computers based on the number of instruction and data streams as SISD, SIMD, MISD, and MIMD. It then provides details about each classification including characteristics, examples, and limitations. The document also covers topics like pipelining, interconnection networks, and how pipelining can improve the speed of computation.
The document provides an overview of pipelining in computer processors. It discusses how pipelining works by dividing processor operations like fetch, decode, execute, memory, and write-back into discrete stages that can overlap, improving throughput. Key points made include:
- Pipelining allows multiple instructions to be in different stages of completion at the same time, improving instruction throughput.
- The document uses an example of a sequential laundry process versus a pipelined laundry process to illustrate how pipelining improves efficiency.
- It describes the five main stages of a RISC instruction set pipeline - fetch, decode, execute, memory, and write-back. The work done and data passed between each stage
This document provides an introduction to parallel and pipeline computer processors. It begins by classifying parallel processors according to Flynn's taxonomy, which considers the number of instruction and data streams that can be processed simultaneously. The main categories are single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD). Pipeline processing is also introduced as a form of parallelism. Computational models for parallel processing including speedup and Amdahl's law are discussed. Finally, instruction pipelining is explained through an example showing how nine instructions can be processed in 14 time units using a six-stage pipeline instead of 54 time units sequentially.
This document discusses instruction pipelining and main memory. It begins by explaining how an instruction pipeline works, overlapping the fetch, decode, and execute phases of instruction processing. It notes some difficulties in pipelining including resource conflicts, data dependencies, and branch instructions. It then discusses pipeline control and performance, noting that pipelining provides faster processing by decomposing tasks into sequential sub-operations that can overlap. It concludes by answering questions about pipelining hazards and calculating pipeline metrics for example processors.
This document discusses general-purpose processors. It begins by introducing general-purpose processors and their basic architecture, which consists of a control unit and datapath that is designed to perform a variety of computation tasks. It then describes the operations of loading, storing, and arithmetic/logical operations that can be performed by the datapath. Subsequent sections provide more details on the control unit and how it sequences operations, instruction cycles, architectural considerations like bit-width and clock frequency, and techniques for improving performance like pipelining and superscalar execution. The document concludes with sections on assembly-level instructions and programmer considerations.
This document discusses parallel processing techniques such as pipelining and vector processing to increase computational speed. It covers Flynn's classification of computer architectures, arithmetic pipelining using a floating-point adder as an example, instruction pipelining with a four-segment model, resolving data dependencies and branch difficulties in pipelines, and RISC pipeline examples addressing delayed load and branch issues. The key techniques discussed are decomposing operations into parallel suboperations, hardware interlocks, operand forwarding, and compiler assistance.
The document discusses the basics of RISC instruction set architectures and pipelining in CPUs. It begins by describing properties of RISC ISAs, including that operations apply to full registers, only load/store instructions affect memory, and instructions are typically one size. It then describes different types of RISC instructions like ALU, load/store, and branches. The document goes on to explain the implementation of a RISC pipeline in 5 stages and the concept of pipelining to improve CPU performance by overlapping instruction execution. It also discusses potential hazards that can degrade pipeline performance like structural, data, and control hazards.
The document discusses RISC instruction set basics and pipelining concepts. It begins by describing properties of RISC architectures, including that operations apply to full registers and only load/store instructions affect memory. It then describes different types of RISC instructions like ALU, load/store, and branches. The document goes on to explain the implementation of instructions in a MIPS64 pipeline with 5 stages: instruction fetch, decode/register fetch, execute, memory access, and write-back. It concludes by defining pipelining and describing how it can increase throughput by overlapping instruction execution.
Microchip's PIC Micro Controller - Presentation Covers- Embedded system,Application, Harvard and Von Newman Architecture, PIC Microcontroller Instruction Set, PIC assembly language programming, PIC Basic circuit design and its programming etc.
The document discusses digital signal processors and digital signal controllers. It begins with an overview of microprocessors and microcontrollers before explaining that digital signal processors have additional hardware units that allow them to perform mathematical operations more efficiently compared to general purpose microprocessors. Digital signal controllers combine the computational power of a digital signal processor with integrated memory and peripherals on a single chip for embedded real-time control applications involving intensive math operations. Texas Instruments is a major manufacturer of digital signal processors and produces the C2000 family of digital signal controllers.
The document discusses pipeline computing and its various types and applications. It defines pipeline computing as a technique to decompose a sequential process into parallel sub-processes that can execute concurrently. There are two main types - linear and non-linear pipelines. Linear pipelines use a single reservation table while non-linear pipelines use multiple tables. Common applications of pipeline computing include instruction pipelines in CPUs, graphics pipelines in GPUs, software pipelines using pipes, and HTTP pipelining. The document also discusses implementations of pipeline computing and its advantages like reduced cycle time and increased instruction throughput.
This document summarizes several course projects completed by Setiawan Soekamtoputra for their Master's degree. The projects include:
1) Design of a 32-bit pipelined CPU in Verilog including implementation of an ASIC flow, multiplier with accumulator case study, and pipeline optimization case study.
2) Development of a monitor program for the MC68000 processor in assembly language including common memory and register commands and exception handlers.
3) Implementation of a high-performance pipelined MIPS processor in VHDL including hazard detection and data forwarding units to handle data and branch hazards.
4) Network on chip prototype designs including a 3-node partially connected mesh design in SystemC and
This document discusses pipelining techniques used to improve the performance of arithmetic and instruction processing. It describes how pipelining decomposes operations into sequential sub-operations that can execute concurrently across multiple pipeline stages. In arithmetic pipelining, it provides examples of a floating point adder broken into four stages: compare exponents, align mantissa, add/subtract mantissa, and normalize result. For instruction pipelining, it outlines a four-stage RISC pipeline of fetch, decode/address, fetch operands, and execute stages and how dependencies are handled through techniques like delayed loading and branching.
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We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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2. 2
Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
PARALLEL PROCESSING
Levels of Parallel Processing
- Job or Program level
- Task or Procedure level
- Inter-Instruction level
- Intra-Instruction level
Execution of Concurrent Events in the computing
process to achieve faster Computational Speed
Parallel Processing
3. 3
Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
PARALLEL COMPUTERS
Architectural Classification
Number of Data Streams
Number of
Instruction
Streams
Single
Multiple
Single Multiple
SISD SIMD
MISD MIMD
* Flynn's classification
- Based on the multiplicity of Instruction Streams and Data Streams
- Instruction Stream
Sequence of Instructions read from memory
- Data Stream
Operations performed on the data in the processor
Parallel Processing
4. 4
Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
COMPUTER ARCHITECTURES FOR PARALLEL PROCESSING
Von-Neuman
based
Dataflow
Reduction
SISD
MISD
SIMD
MIMD
Superscalar processors
Superpipelined processors
VLIW
Nonexistence
Array processors
Systolic arrays
Associative processors
Shared-memory multiprocessors
Bus based
Crossbar switch based
Multistage IN based
Message-passing multicomputers
Hypercube
Mesh
Reconfigurable
Parallel Processing
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Pipelining and Vector Processing
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SISD COMPUTER SYSTEMS
Control
Unit
Processor
Unit
Memory
Instruction stream
Data stream
Characteristics
- Standard von Neumann machine
- Instructions and data are stored in memory
- One operation at a time
Limitations
Von Neumann bottleneck
Maximum speed of the system is limited by the
Memory Bandwidth (bits/sec or bytes/sec)
- Limitation on Memory Bandwidth
- Memory is shared by CPU and I/O
Parallel Processing
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Pipelining and Vector Processing
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MISD COMPUTER SYSTEMS
M CU P
M CU P
M CU P
•
•
•
•
•
•
Memory
Instruction stream
Data stream
Characteristics
- There is no computer at present that can be
classified as MISD
Parallel Processing
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Pipelining and Vector Processing
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SIMD COMPUTER SYSTEMS
Control Unit
Memory
Alignment network
P P P
• • •
M M
M • • •
Data bus
Instruction stream
Data stream
Processor units
Memory modules
Characteristics
- Only one copy of the program exists
- A single controller executes one instruction at a time
Parallel Processing
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Pipelining and Vector Processing
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TYPES OF SIMD COMPUTERS
Array Processors
- The control unit broadcasts instructions to all PEs,
and all active PEs execute the same instructions
- ILLIAC IV, GF-11, Connection Machine, DAP, MPP
Systolic Arrays
- Regular arrangement of a large number of
very simple processors constructed on
VLSI circuits
- CMU Warp, Purdue CHiP
Associative Processors
- Content addressing
- Data transformation operations over many sets
of arguments with a single instruction
- STARAN, PEPE
Parallel Processing
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
MIMD COMPUTER SYSTEMS
Interconnection Network
P M P M
P M • • •
Shared Memory
Characteristics
- Multiple processing units
- Execution of multiple instructions on multiple data
Types of MIMD computer systems
- Shared memory multiprocessors
- Message-passing multicomputers
Parallel Processing
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Pipelining and Vector Processing
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SHARED MEMORY MULTIPROCESSORS
Characteristics
All processors have equally direct access to
one large memory address space
Example systems
Bus and cache-based systems
- Sequent Balance, Encore Multimax
Multistage IN-based systems
- Ultracomputer, Butterfly, RP3, HEP
Crossbar switch-based systems
- C.mmp, Alliant FX/8
Limitations
Memory access latency
Hot spot problem
Interconnection Network(IN)
• • •
• • •
P P
P
M M
M
Buses,
Multistage IN,
Crossbar Switch
Parallel Processing
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
MESSAGE-PASSING MULTICOMPUTER
Characteristics
- Interconnected computers
- Each processor has its own memory, and
communicate via message-passing
Example systems
- Tree structure: Teradata, DADO
- Mesh-connected: Rediflow, Series 2010, J-Machine
- Hypercube: Cosmic Cube, iPSC, NCUBE, FPS T Series, Mark III
Limitations
- Communication overhead
- Hard to programming
Message-Passing Network
• • •
P P
P
M M M
• • •
Point-to-point connections
Parallel Processing
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Pipelining and Vector Processing
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PIPELINING
R1 Ai, R2 Bi Load Ai and Bi
R3 R1 * R2, R4 Ci Multiply and load Ci
R5 R3 + R4 Add
A technique of decomposing a sequential process
into suboperations, with each subprocess being
executed in a partial dedicated segment that
operates concurrently with all other segments.
Ai * Bi + Ci for i = 1, 2, 3, ... , 7
Ai
R1 R2
Multiplier
R3 R4
Adder
R5
Memory
Pipelining
Bi Ci
Segment 1
Segment 2
Segment 3
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Computer Organization Computer Architectures Lab
GENERAL PIPELINE
General Structure of a 4-Segment Pipeline
S R
1 1 S R
2 2 S R
3 3 S R
4 4
Input
Clock
Space-Time Diagram
1 2 3 4 5 6 7 8 9
T1
T1
T1
T1
T2
T2
T2
T2
T3
T3
T3
T3 T4
T4
T4
T4 T5
T5
T5
T5 T6
T6
T6
T6
Clock cycles
Segment 1
2
3
4
Pipelining
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Computer Organization Computer Architectures Lab
PIPELINE SPEEDUP
n: Number of tasks to be performed
Conventional Machine (Non-Pipelined)
tn: Clock cycle
t1: Time required to complete the n tasks
t1 = n * tn
Pipelined Machine (k stages)
tp: Clock cycle (time to complete each suboperation)
tk: Time required to complete the n tasks
tk = (k + n - 1) * tp
Speedup
Sk: Speedup
Sk = n*tn / (k + n - 1)*tp
n
Sk =
tn
tp
( = k, if tn = k * tp )
lim
Pipelining
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
PIPELINE AND MULTIPLE FUNCTION UNITS
P1
I i
P2
Ii+1
P3
I i+2
P4
I i+3
Multiple Functional Units
Example
- 4-stage pipeline
- subopertion in each stage; tp = 20nS
- 100 tasks to be executed
- 1 task in non-pipelined system; 20*4 = 80nS
Pipelined System
(k + n - 1)*tp = (4 + 99) * 20 = 2060nS
Non-Pipelined System
n*k*tp = 100 * 80 = 8000nS
Speedup
Sk = 8000 / 2060 = 3.88
4-Stage Pipeline is basically identical to the system
with 4 identical function units
Pipelining
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ARITHMETIC PIPELINE
Floating-point adder
[1] Compare the exponents
[2] Align the mantissa
[3] Add/sub the mantissa
[4] Normalize the result
X = A x 2a
Y = B x 2b
R
Compare
exponents
by subtraction
a b
R
Choose exponent
Exponents
R
A B
Align mantissa
Mantissas
Difference
R
Add or subtract
mantissas
R
Normalize
result
R
R
Adjust
exponent
R
Segment 1:
Segment 2:
Segment 3:
Segment 4:
Arithmetic Pipeline
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
4-STAGE FLOATING POINT ADDER
A = a x 2p B = b x 2q
p a q b
Exponent
subtractor
Fraction
selector
Fraction with min(p,q)
Right shifter
Other
fraction
t = |p - q|
r = max(p,q)
Fraction
adder
Leading zero
counter
r c
Left shifter
c
Exponent
adder
r
s d
d
Stages:
S1
S2
S3
S4
C = A + B = c x 2 = d x 2
r s
(r = max (p,q), 0.5 d < 1)
Arithmetic Pipeline
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
INSTRUCTION CYCLE
Six Phases* in an Instruction Cycle
[1] Fetch an instruction from memory
[2] Decode the instruction
[3] Calculate the effective address of the operand
[4] Fetch the operands from memory
[5] Execute the operation
[6] Store the result in the proper place
* Some instructions skip some phases
* Effective address calculation can be done in
the part of the decoding phase
* Storage of the operation result into a register
is done automatically in the execution phase
==> 4-Stage Pipeline
[1] FI: Fetch an instruction from memory
[2] DA: Decode the instruction and calculate
the effective address of the operand
[3] FO: Fetch the operand
[4] EX: Execute the operation
Instruction Pipeline
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
INSTRUCTION PIPELINE
Execution of Three Instructions in a 4-Stage Pipeline
Instruction Pipeline
FI DA FO EX
FI DA FO EX
FI DA FO EX
i
i+1
i+2
Conventional
Pipelined
FI DA FO EX
FI DA FO EX
FI DA FO EX
i
i+1
i+2
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Pipelining and Vector Processing
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INSTRUCTION EXECUTION IN A 4-STAGE PIPELINE
1 2 3 4 5 6 7 8 9 10 12 13
11
FI DA FO EX
1
FI DA FO EX
FI DA FO EX
FI DA FO EX
FI DA FO EX
FI DA FO EX
FI DA FO EX
2
3
4
5
6
7
FI
Step:
Instruction
(Branch)
Instruction Pipeline
Fetch instruction
from memory
Decode instruction
and calculate
effective address
Branch?
Fetch operand
from memory
Execute instruction
Interrupt?
Interrupt
handling
Update PC
Empty pipe
no
yes
yes
no
Segment1:
Segment2:
Segment3:
Segment4:
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
MAJOR HAZARDS IN PIPELINED EXECUTION
Structural hazards(Resource Conflicts)
Hardware Resources required by the instructions in
simultaneous overlapped execution cannot be met
Data hazards (Data Dependency Conflicts)
An instruction scheduled to be executed in the pipeline requires the
result of a previous instruction, which is not yet available
JMP ID PC + PC
bubble IF ID OF OE OS
Branch address dependency
Hazards in pipelines may make it
necessary to stall the pipeline
Pipeline Interlock:
Detect Hazards Stall until it is cleared
Instruction Pipeline
ADD DA B,C +
INC DA +1
R1
bubble
Data dependency
R1 <- B + C
R1 <- R1 + 1
Control hazards
Branches and other instructions that change the PC
make the fetch of the next instruction to be delayed
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Pipelining and Vector Processing
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STRUCTURAL HAZARDS
Structural Hazards
Occur when some resource has not been
duplicated enough to allow all combinations
of instructions in the pipeline to execute
Example: With one memory-port, a data and an instruction fetch
cannot be initiated in the same clock
The Pipeline is stalled for a structural hazard
<- Two Loads with one port memory
-> Two-port memory will serve without stall
Instruction Pipeline
FI DA FO EX
i
i+1
i+2
FI DA FO EX
FI DA FO EX
stall
stall
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DATA HAZARDS
Data Hazards
Occurs when the execution of an instruction
depends on the results of a previous instruction
ADD R1, R2, R3
SUB R4, R1, R5
Hardware Technique
Interlock
- hardware detects the data dependencies and delays the scheduling
of the dependent instruction by stalling enough clock cycles
Forwarding (bypassing, short-circuiting)
- Accomplished by a data path that routes a value from a source
(usually an ALU) to a user, bypassing a designated register. This
allows the value to be produced to be used at an earlier stage in the
pipeline than would otherwise be possible
Software Technique
Instruction Scheduling(compiler) for delayed load
Data hazard can be dealt with either hardware
techniques or software technique
Instruction Pipeline
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
FORWARDING HARDWARE
Register
file
Result
write bus
Bypass
path
ALU result buffer
MUX
ALU
R4
MUX
Instruction Pipeline
Example:
ADD R1, R2, R3
SUB R4, R1, R5
3-stage Pipeline
I: Instruction Fetch
A: Decode, Read Registers,
ALU Operations
E: Write the result to the
destination register
I A E
ADD
SUB I A E Without Bypassing
I A E
SUB With Bypassing
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
INSTRUCTION SCHEDULING
a = b + c;
d = e - f;
Unscheduled code:
Delayed Load
A load requiring that the following instruction not use its result
Scheduled Code:
LW Rb, b
LW Rc, c
LW Re, e
ADD Ra, Rb, Rc
LW Rf, f
SW a, Ra
SUB Rd, Re, Rf
SW d, Rd
LW Rb, b
LW Rc, c
ADD Ra, Rb, Rc
SW a, Ra
LW Re, e
LW Rf, f
SUB Rd, Re, Rf
SW d, Rd
Instruction Pipeline
28. 28
Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
CONTROL HAZARDS
Branch Instructions
- Branch target address is not known until
the branch instruction is completed
- Stall -> waste of cycle times
FI DA FO EX
FI DA FO EX
Branch
Instruction
Next
Instruction
Target address available
Dealing with Control Hazards
* Prefetch Target Instruction
* Branch Target Buffer
* Loop Buffer
* Branch Prediction
* Delayed Branch
Instruction Pipeline
29. 29
Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
CONTROL HAZARDS
Instruction Pipeline
Prefetch Target Instruction
– Fetch instructions in both streams, branch not taken and branch taken
– Both are saved until branch branch is executed. Then, select the right
instruction stream and discard the wrong stream
Branch Target Buffer(BTB; Associative Memory)
– Entry: Addr of previously executed branches; Target instruction
and the next few instructions
– When fetching an instruction, search BTB.
– If found, fetch the instruction stream in BTB;
– If not, new stream is fetched and update BTB
Loop Buffer(High Speed Register file)
– Storage of entire loop that allows to execute a loop without accessing memory
Branch Prediction
– Guessing the branch condition, and fetch an instruction stream based on
the guess. Correct guess eliminates the branch penalty
Delayed Branch
– Compiler detects the branch and rearranges the instruction sequence
by inserting useful instructions that keep the pipeline busy
in the presence of a branch instruction
30. 30
Pipelining and Vector Processing
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RISC PIPELINE
Instruction Cycles of Three-Stage Instruction Pipeline
RISC Pipeline
RISC
- Machine with a very fast clock cycle that
executes at the rate of one instruction per cycle
<- Simple Instruction Set
Fixed Length Instruction Format
Register-to-Register Operations
Data Manipulation Instructions
I: Instruction Fetch
A: Decode, Read Registers, ALU Operations
E: Write a Register
Load and Store Instructions
I: Instruction Fetch
A: Decode, Evaluate Effective Address
E: Register-to-Memory or Memory-to-Register
Program Control Instructions
I: Instruction Fetch
A: Decode, Evaluate Branch Address
E: Write Register(PC)
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Pipelining and Vector Processing
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DELAYED LOAD
Three-segment pipeline timing
Pipeline timing with data conflict
clock cycle 1 2 3 4 5 6
Load R1 I A E
Load R2 I A E
Add R1+R2 I A E
Store R3 I A E
Pipeline timing with delayed load
clock cycle 1 2 3 4 5 6 7
Load R1 I A E
Load R2 I A E
NOP I A E
Add R1+R2 I A E
Store R3 I A E
LOAD: R1 M[address 1]
LOAD: R2 M[address 2]
ADD: R3 R1 + R2
STORE: M[address 3] R3
RISC Pipeline
The data dependency is taken
care by the compiler rather
than the hardware
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Pipelining and Vector Processing
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DELAYED BRANCH
1
I
3 4 6
5
2
Clock cycles:
1. Load A
2. Increment
4. Subtract
5. Branch to X
7
3. Add
8
6. NOP
E
I A E
I A E
I A E
I A E
I A E
9 10
7. NOP
8. Instr. in X
I A E
I A E
1
I
3 4 6
5
2
Clock cycles:
1. Load A
2. Increment
4. Add
5. Subtract
7
3. Branch to X
8
6. Instr. in X
E
I A E
I A E
I A E
I A E
I A E
Compiler analyzes the instructions before and after
the branch and rearranges the program sequence by
inserting useful instructions in the delay steps
Using no-operation instructions
Rearranging the instructions
RISC Pipeline
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Pipelining and Vector Processing
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VECTOR PROCESSING
Vector Processing
Vector Processing Applications
• Problems that can be efficiently formulated in terms of vectors
– Long-range weather forecasting
– Petroleum explorations
– Seismic data analysis
– Medical diagnosis
– Aerodynamics and space flight simulations
– Artificial intelligence and expert systems
– Mapping the human genome
– Image processing
Vector Processor (computer)
Ability to process vectors, and related data structures such as matrices
and multi-dimensional arrays, much faster than conventional computers
Vector Processors may also be pipelined
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VECTOR PROGRAMMING
DO 20 I = 1, 100
20 C(I) = B(I) + A(I)
Conventional computer
Initialize I = 0
20 Read A(I)
Read B(I)
Store C(I) = A(I) + B(I)
Increment I = i + 1
If I 100 goto 20
Vector computer
C(1:100) = A(1:100) + B(1:100)
Vector Processing
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Pipelining and Vector Processing
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VECTOR INSTRUCTIONS
f1: V * V
f2: V * S
f3: V x V * V
f4: V x S * V
V: Vector operand
S: Scalar operand
Type Mnemonic Description (I = 1, ..., n)
Vector Processing
f1 VSQR Vector square root B(I) * SQR(A(I))
VSIN Vector sine B(I) * sin(A(I))
VCOM Vector complement A(I) * A(I)
f2 VSUM Vector summation S * S A(I)
VMAX Vector maximum S * max{A(I)}
f3 VADD Vector add C(I) * A(I) + B(I)
VMPY Vector multiply C(I) * A(I) * B(I)
VAND Vector AND C(I) * A(I) . B(I)
VLAR Vector larger C(I) * max(A(I),B(I))
VTGE Vector test > C(I) * 0 if A(I) < B(I)
C(I) * 1 if A(I) > B(I)
f4 SADD Vector-scalar add B(I) * S + A(I)
SDIV Vector-scalar divide B(I) * A(I) / S
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Pipelining and Vector Processing
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VECTOR INSTRUCTION FORMAT
Operation
code
Base address
source 1
Base address
source 2
Base address
destination
Vector
length
Vector Processing
Vector Instruction Format
Source
A
Source
B
Multiplier
pipeline
Adder
pipeline
Pipeline for Inner Product
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Pipelining and Vector Processing
Computer Organization Computer Architectures Lab
MULTIPLE MEMORY MODULE AND INTERLEAVING
Vector Processing
Multiple Module Memory
Address Interleaving
Different sets of addresses are assigned to
different memory modules
AR
Memory
array
DR
AR
Memory
array
DR
AR
Memory
array
DR
AR
Memory
array
DR
Address bus
Data bus
M0 M1 M2 M3