Computer Architecture Chapter 7: Strip Mining Quiz and Flashcards

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Questions and Answers

What is the primary function of the vector load/store unit?

To load and store vectors to and from the memory (correct)

To detect hazards in vector operations

To perform arithmetic operations on vectors

To handle scalar loads and stores

What is the purpose of the control unit in vector computers?

To store vector registers

To perform arithmetic operations on vectors

To detect hazards and control the flow of vector operations (correct)

To handle vector loads and stores

How many general-purpose registers are available in this architecture?

31 (correct)

What is the purpose of the vector add instruction?

To add elements of two vectors Signup and view all the answers

What is the purpose of the vector div instruction?

To divide elements of one vector by another Signup and view all the answers

What is the function of the vector functional units?

To perform arithmetic operations on vectors Signup and view all the answers

What is the purpose of the vld instruction?

To load a vector register from memory Signup and view all the answers

What is the primary advantage of pipelining in vector computers?

Ability to start a new operation on every clock cycle Signup and view all the answers

What is the function of the scalar registers?

To provide data to the vector functional units Signup and view all the answers

What is the bandwidth of the vector load/store unit?

One word per clock cycle Signup and view all the answers

What is the relationship between the number of clock cycles and the bandwidth of the vector load/store unit?

The bandwidth of the vector load/store unit is one word per clock cycle. Signup and view all the answers

How do the vector functional units handle new operations?

Each unit is fully pipelined and able to start a new operation on every clock cycle. Signup and view all the answers

What is the purpose of the scalar registers in relation to the vector functional units?

Scalar registers provide data as input to the vector functional units. Signup and view all the answers

What is the primary difference between the vadd and vsub instructions?

The vadd instruction adds elements, while the vsub instruction subtracts elements. Signup and view all the answers

How do the vector load and store instructions interact with memory?

The vector load instruction loads a vector register from memory, and the vector store instruction stores a vector register to memory. Signup and view all the answers

What is the purpose of the control unit in relation to hazards?

The control unit detects hazards, including structural hazards for functional units and data hazards on register accesses. Signup and view all the answers

How do the vector add and subtract instructions differ in their operand usage?

The vadd instruction adds elements of two vector registers, while the vsub instruction subtracts elements of one vector register from another. Signup and view all the answers

What is the primary difference between the vdiv instruction and the other arithmetic instructions?

The vdiv instruction performs division, whereas the other arithmetic instructions perform addition and subtraction. Signup and view all the answers

How do the vector registers interact with the memory during vector load and store operations?

The vector registers load data from memory during a vector load operation and store data to memory during a vector store operation. Signup and view all the answers

What is the role of the initial latency in the vector load/store unit?

The initial latency occurs before the vector load/store unit can move words between vector registers and memory at a bandwidth of one word per clock cycle. Signup and view all the answers

What is the primary function of the vector-length register (VL) in vector computers?

To control the maximum vector length (MVL) of a vector operation Signup and view all the answers

What happens when the value of n is unknown at compile time and is assigned to a value greater than MVL?

The strip mining technique is used to handle the unknown value Signup and view all the answers

What is the purpose of the modulo operation in the strip mining technique?

To find the remainder iteration in the loop Signup and view all the answers

What is the relationship between the vector-length register (VL) and the maximum vector length (MVL)?

VL is always less than or equal to MVL Signup and view all the answers

What is the advantage of using strip mining in vector computers?

It allows for efficient handling of unknown vector lengths Signup and view all the answers

What is the primary difference between the main loop and the remainder loop in strip mining?

The main loop handles the iterations in blocks of MVL, while the remainder loop handles the odd-size piece Signup and view all the answers

What happens to the vector operation when the value of n is less than or equal to MVL?

The vector operation is executed in a single iteration Signup and view all the answers

What is the purpose of the variable 'm' in the strip mining technique?

To find the remainder iteration in the loop Signup and view all the answers

What is the advantage of using a vector-length register (VL) in vector computers?

It allows for efficient handling of unknown vector lengths Signup and view all the answers

What is the primary difference between the vector-length register (VL) and the maximum vector length (MVL)?

VL is a register, while MVL is a constant Signup and view all the answers

What is the significance of the Vector-Length Register (VL) in controlling the length of vector operations?

The Vector-Length Register (VL) controls the length of any vector operation, including vector load/store, and is limited by the Maximum Vector Length (MVL). Signup and view all the answers

How does the strip mining technique handle vector operations when n is unknown at compile time and may be greater than MVL?

Strip mining involves generating code where each vector operation is done for a size less than or equal to MVL, with one loop handling iterations that are a multiple of MVL and another loop handling the remainder. Signup and view all the answers

What is the purpose of the modulo operation in the strip mining technique?

The modulo operation (n % MVL) is used to find the odd-size piece of the vector, which is then processed separately. Signup and view all the answers

How do the main loop and remainder loop differ in the strip mining technique?

The main loop handles iterations that are a multiple of MVL, while the remainder loop handles the remaining iterations that are less than MVL. Signup and view all the answers

What happens when the value of n is less than or equal to MVL?

The vector operation can be performed in a single pass, without the need for strip mining. Signup and view all the answers

What is the role of the variable 'm' in the strip mining technique?

The variable 'm' represents the remainder of the division of n by MVL, and is used to determine the number of remaining iterations. Signup and view all the answers

What is the advantage of using strip mining in vector computers?

Strip mining allows for efficient vector processing even when the vector length is unknown or variable. Signup and view all the answers

How does the Vector-Length Register (VL) interact with the Maximum Vector Length (MVL)?

The VL register is limited by the MVL, and ensures that vector operations do not exceed the MVL. Signup and view all the answers

What is the significance of the Maximum Vector Length (MVL) in vector computers?

The MVL determines the maximum length of a vector that can be processed in a single operation. Signup and view all the answers

How does the strip mining technique improve the efficiency of vector computers?

Strip mining allows for efficient vector processing by breaking down large vectors into smaller, manageable chunks. Signup and view all the answers

What is the purpose of computing the corresponding element index i in the CUDA function?

To compute the thread ID for each element in the vector Signup and view all the answers

What is the role of the blockIdx.x variable in the CUDA function?

It represents the block ID in the grid Signup and view all the answers

How many threads are launched in the CUDA code in Listing 7.18?

nblocks * iBlockSize Signup and view all the answers

What is the purpose of the daxpy function in the CUDA code?

To multiply two vectors together Signup and view all the answers

What is the relationship between the block size and the number of threads per block?

The block size is equal to the number of threads per block Signup and view all the answers

What is the purpose of the grid in the CUDA code?

To launch a set of thread blocks Signup and view all the answers

How many thread blocks are used in the example with 8,192 elements?

16 Signup and view all the answers

What is the purpose of the blockIdx.x variable in the CUDA function?

To compute the block ID in the grid Signup and view all the answers

What is the advantage of using a multithreaded SIMD processor?

Improved vector processing efficiency Signup and view all the answers

What is the purpose of the daxpy function in the CUDA code?

To multiply two vectors together Signup and view all the answers

What is the significance of the number 256 in the CUDA code in Listing 7.18?

It is the number of threads per block, which determines the block size and affects the performance of the multithreaded SIMD processor. Signup and view all the answers

How does the CUDA function compute the corresponding element index i?

It uses the block ID, number of threads per block, and thread ID to compute the index i. Signup and view all the answers

What is the purpose of the grid in the CUDA code?

It is a collection of thread blocks that execute the same kernel function on a large dataset. Signup and view all the answers

What is the relationship between the number of threads launched and the number of elements in the vector?

The number of threads launched is equal to the number of elements in the vector, as each thread processes one element. Signup and view all the answers

What is the advantage of using a multithreaded SIMD processor in the CUDA code?

It enables the parallel execution of the same instruction on multiple data elements, improving the performance and efficiency of the code. Signup and view all the answers

How does the CUDA code handle the multiplication and addition operation in the DAXPY function?

It performs the operation as long as the index i is within the array, using the expression y[i] = a * x[i] + y[i]. Signup and view all the answers

What is the role of the block ID in the CUDA function?

It is used to compute the corresponding element index i, along with the number of threads per block and thread ID. Signup and view all the answers

What is the significance of the number 512 in the example with 8,192 elements?

It is the maximum number of threads per block, which determines the number of thread blocks required to process the entire dataset. Signup and view all the answers

What is the purpose of the DAXPY function in the CUDA code?

It performs the operation of multiplying a vector by a scalar and adding the result to another vector, in parallel. Signup and view all the answers

How does the CUDA code handle the case where the number of elements in the vector is not a multiple of the block size?

It uses the expression (n + iBlockSize - 1) / iBlockSize to compute the number of blocks, which ensures that all elements are processed. Signup and view all the answers

Study Notes

Flynn's Taxonomy

Flynn's taxonomy classifies computer architectures into four categories: SISD, SIMD, MISD, and MIMD.
SISD (Single Instruction, Single Data) is related to single processors, where one instruction works on one data at a time.
SIMD (Single Instruction, Multiple Data) is related to vector architectures and graphics processing units, where one instruction works on multiple data at the same time.
MISD (Multiple Instructions, Single Data) is related to fault tolerance, where different instructions handle one data.
MIMD (Multiple Instructions, Multiple Data) is related to multiprocessors, where different instructions work on different data in parallel.

SIMD vs MIMD

MIMD architecture needs to fetch one instruction per data operation, providing more flexibility.
SIMD architecture is potentially more energy-efficient than MIMD, with a single instruction launching many data operations.
In SIMD, the programmer thinks sequentially, achieving parallel speedup by having parallel data operations.

SIMD Vector Processors

SIMD vector processors have high-level instructions operating on vectors, such as in equation (7.1).
These processors are particularly useful for scientific and engineering applications, simulations, and multimedia applications.

Vector Processor Characteristics

The parallelism of loops can be exposed by the programmer or compiler through the usage of vector instructions.
The memory system is adapted to provide memory access to a whole vector instead of individual elements.
Hardware only needs to check data hazards between two vector instructions once per vector operand.
Control hazards are eliminated, and dependency verification logic is similar to scalar instructions.

Basic Architecture

A vector processor consists of a scalar unit and vector units.
The RISC-V vector instruction set extension (RV64V) has 32 vector registers and a scalar unit.
Each vector register holds a single vector, and the vector register file provides a sufficient number of ports to feed all vector functional units.

Vector Instructions

Examples of vector instructions include vadd, vsub, vdiv, and vld, which operate on vectors.
These instructions perform operations on entire vectors, reducing the need for control hazards and dependency verification.

Flynn's Taxonomy

Flynn's taxonomy classifies computer architectures into four categories: SISD, SIMD, MISD, and MIMD.
SISD (Single Instruction, Single Data) is related to single processors, where one instruction works on one data at a time.
SIMD (Single Instruction, Multiple Data) is related to vector architectures and graphics processing units, where one instruction works on multiple data at the same time.
MISD (Multiple Instructions, Single Data) is related to fault tolerance, where different instructions handle one data.
MIMD (Multiple Instructions, Multiple Data) is related to multiprocessors, where different instructions work on different data in parallel.

SIMD vs MIMD

MIMD architecture needs to fetch one instruction per data operation, providing more flexibility.
SIMD architecture is potentially more energy-efficient than MIMD, with a single instruction launching many data operations.
In SIMD, the programmer thinks sequentially, achieving parallel speedup by having parallel data operations.

SIMD Vector Processors

SIMD vector processors have high-level instructions operating on vectors, such as in equation (7.1).
These processors are particularly useful for scientific and engineering applications, simulations, and multimedia applications.

Vector Processor Characteristics

The parallelism of loops can be exposed by the programmer or compiler through the usage of vector instructions.
The memory system is adapted to provide memory access to a whole vector instead of individual elements.
Hardware only needs to check data hazards between two vector instructions once per vector operand.
Control hazards are eliminated, and dependency verification logic is similar to scalar instructions.

Basic Architecture

A vector processor consists of a scalar unit and vector units.
The RISC-V vector instruction set extension (RV64V) has 32 vector registers and a scalar unit.
Each vector register holds a single vector, and the vector register file provides a sufficient number of ports to feed all vector functional units.

Vector Instructions

Examples of vector instructions include vadd, vsub, vdiv, and vld, which operate on vectors.
These instructions perform operations on entire vectors, reducing the need for control hazards and dependency verification.

Vector Computers

A vector loop for RV64V is shown in Eq.(7.1): ~ = a×X + Y, where X and Y are vectors of size n, and a is a scalar.
This problem is known as the SAXPY (Single-precision a × X plus Y) or DAXPY (Double-precision a × X plus Y) loop, which forms the inner loop of the Linpack benchmark.

RV64G vs. RV64V

The code in Listing 7.6 shows the implementation of the DAXPY loop using the RV64G ISA.
The code in Listing 7.7 shows the implementation of the DAXPY loop using the RV64V ISA.
The RV64V code has only 8 instructions, while the RV64G code has 258 instructions (32 iterations × 8 instructions + 2 setup instructions).
The reduction in instructions is due to vector operations working on 32 elements.

Vector Instructions Optimizations

Stalls in vector instructions are different from the RV64G ISA.
In the RV64V code, an instruction stalls only for the first element in each vector, whereas in the RV64G code, every fadd.d instruction must wait for a fmul.d to avoid a RAW dependence.
Chaining allows forwarding of element-dependent operations in vector operations.
Convoys are sets of vector instructions that could execute together, without structural hazards or RAW hazards.

Vector Processors Architecture

Modern vector computers have vector functional units with multiple parallel pipelines, i.e., lanes.
Lanes are able to produce two or more results per clock cycle.
Each lane contains one portion of the vector register file and one execution pipeline from each vector functional unit.

Vector-Length Registers

A vector register's processor has a natural vector length determined by the maximum vector length (MVL).
The vector-length register (VL) controls the length of any vector operation, including vector load/store.

Strip Mining

Strip mining is a technique used when the vector size is unknown at compile time and may be greater than MVL.
Strip mining consists of generating code where each vector operation is done for a size less than or equal to MVL.
One loop handles any number of iterations that is a multiple of MVL, and another loop handles the remainder iteration, which must be less than the MVL size.

Vector Computers

A vector loop for RV64V is shown in Eq.(7.1): ~ = a×X + Y, where X and Y are vectors of size n, and a is a scalar.
This problem is known as the SAXPY (Single-precision a × X plus Y) or DAXPY (Double-precision a × X plus Y) loop, which forms the inner loop of the Linpack benchmark.

RV64G vs. RV64V

The code in Listing 7.6 shows the implementation of the DAXPY loop using the RV64G ISA.
The code in Listing 7.7 shows the implementation of the DAXPY loop using the RV64V ISA.
The RV64V code has only 8 instructions, while the RV64G code has 258 instructions (32 iterations × 8 instructions + 2 setup instructions).
The reduction in instructions is due to vector operations working on 32 elements.

Vector Instructions Optimizations

Stalls in vector instructions are different from the RV64G ISA.
In the RV64V code, an instruction stalls only for the first element in each vector, whereas in the RV64G code, every fadd.d instruction must wait for a fmul.d to avoid a RAW dependence.
Chaining allows forwarding of element-dependent operations in vector operations.
Convoys are sets of vector instructions that could execute together, without structural hazards or RAW hazards.

Vector Processors Architecture

Modern vector computers have vector functional units with multiple parallel pipelines, i.e., lanes.
Lanes are able to produce two or more results per clock cycle.
Each lane contains one portion of the vector register file and one execution pipeline from each vector functional unit.

Vector-Length Registers

A vector register's processor has a natural vector length determined by the maximum vector length (MVL).
The vector-length register (VL) controls the length of any vector operation, including vector load/store.

Strip Mining

Strip mining is a technique used when the vector size is unknown at compile time and may be greater than MVL.
Strip mining consists of generating code where each vector operation is done for a size less than or equal to MVL.
One loop handles any number of iterations that is a multiple of MVL, and another loop handles the remainder iteration, which must be less than the MVL size.

Graphics Processing Units (GPUs)

GPUs are similar to a set of vector processors sharing hardware, with multiple SIMD processors acting as independent MIMD cores.
The main difference between GPUs and vector processors is multithreading, which is fundamental to GPUs.

Programming the GPU

NVIDIA developed a C-like programming language, CUDA, to program its GPUs.
CUDA generates C/C++ code for the system processor (host) and a C/C++ dialect for the GPU (device).
OpenCL is a CUDA-similar programming language, offering a vendor-independent language for multiple platforms.

CUDA Basics

The CUDA thread is the lowest level of parallelism, following the paradigm of "single instruction, multiple threads" (SIMT).
Threads are blocked together and executed in groups, with multithreaded SIMD hardware executing a whole block of threads.
CUDA functions can have different modifiers, such as __device__, __global__, or __host__, to specify where they are executed and launched.

Variables and Function Calls

CUDA variables can have modifiers, such as __device__, to allocate them to the GPU memory and make them accessible by all multithreaded SIMD processors.
CUDA has an extended function call, the CUDA execution configuration, which specifies the dimensions of the code, thread blocks, and threads.

Specific Terms

blockIdx is the identifier/index for blocks.
threadIdx is the identifier/index of the current thread within its block.
blockDim stands for the number of threads in a block, which comes from the dimBlock parameter.

Simple Example

A conventional C code example can be converted to a CUDA version, which launches n threads, one per vector element, with 256 threads per thread block.
The CUDA code computes the corresponding element index i based on the block ID, number of threads per block, and the thread ID.

Simple Example 2

A grid (or vectorized loop) is composed of thread blocks, with each thread block processing up to 512 elements, i.e., 16 threads per block.
The SIMD instruction executes 32 elements at a time, and the example has 16 thread blocks for 8,192 elements in the vectors.

Graphics Processing Units (GPUs)

GPUs are similar to a set of vector processors sharing hardware, with multiple SIMD processors acting as independent MIMD cores.
The main difference between GPUs and vector processors is multithreading, which is fundamental to GPUs.

Programming the GPU

NVIDIA developed a C-like programming language, CUDA, to program its GPUs.
CUDA generates C/C++ code for the system processor (host) and a C/C++ dialect for the GPU (device).
OpenCL is a CUDA-similar programming language, offering a vendor-independent language for multiple platforms.

CUDA Basics

The CUDA thread is the lowest level of parallelism, following the paradigm of "single instruction, multiple threads" (SIMT).
Threads are blocked together and executed in groups, with multithreaded SIMD hardware executing a whole block of threads.
CUDA functions can have different modifiers, such as __device__, __global__, or __host__, to specify where they are executed and launched.

Variables and Function Calls

CUDA variables can have modifiers, such as __device__, to allocate them to the GPU memory and make them accessible by all multithreaded SIMD processors.
CUDA has an extended function call, the CUDA execution configuration, which specifies the dimensions of the code, thread blocks, and threads.

Specific Terms

blockIdx is the identifier/index for blocks.
threadIdx is the identifier/index of the current thread within its block.
blockDim stands for the number of threads in a block, which comes from the dimBlock parameter.

Simple Example

A conventional C code example can be converted to a CUDA version, which launches n threads, one per vector element, with 256 threads per thread block.
The CUDA code computes the corresponding element index i based on the block ID, number of threads per block, and the thread ID.

Simple Example 2

A grid (or vectorized loop) is composed of thread blocks, with each thread block processing up to 512 elements, i.e., 16 threads per block.
The SIMD instruction executes 32 elements at a time, and the example has 16 thread blocks for 8,192 elements in the vectors.

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Questions and Answers

What is the primary function of the vector load/store unit?

What is the purpose of the control unit in vector computers?

How many general-purpose registers are available in this architecture?

What is the purpose of the vector add instruction?

What is the purpose of the vector div instruction?

What is the function of the vector functional units?

What is the purpose of the vld instruction?

What is the primary advantage of pipelining in vector computers?

What is the function of the scalar registers?

What is the bandwidth of the vector load/store unit?

What is the relationship between the number of clock cycles and the bandwidth of the vector load/store unit?

How do the vector functional units handle new operations?

What is the purpose of the scalar registers in relation to the vector functional units?

What is the primary difference between the vadd and vsub instructions?

How do the vector load and store instructions interact with memory?

What is the purpose of the control unit in relation to hazards?

How do the vector add and subtract instructions differ in their operand usage?

What is the primary difference between the vdiv instruction and the other arithmetic instructions?

How do the vector registers interact with the memory during vector load and store operations?

What is the role of the initial latency in the vector load/store unit?

What is the primary function of the vector-length register (VL) in vector computers?

What happens when the value of n is unknown at compile time and is assigned to a value greater than MVL?

What is the purpose of the modulo operation in the strip mining technique?

What is the relationship between the vector-length register (VL) and the maximum vector length (MVL)?

What is the advantage of using strip mining in vector computers?

What is the primary difference between the main loop and the remainder loop in strip mining?

What happens to the vector operation when the value of n is less than or equal to MVL?

What is the purpose of the variable 'm' in the strip mining technique?

What is the advantage of using a vector-length register (VL) in vector computers?

What is the primary difference between the vector-length register (VL) and the maximum vector length (MVL)?

What is the significance of the Vector-Length Register (VL) in controlling the length of vector operations?

How does the strip mining technique handle vector operations when n is unknown at compile time and may be greater than MVL?

What is the purpose of the modulo operation in the strip mining technique?

How do the main loop and remainder loop differ in the strip mining technique?

What happens when the value of n is less than or equal to MVL?

What is the role of the variable 'm' in the strip mining technique?

What is the advantage of using strip mining in vector computers?

How does the Vector-Length Register (VL) interact with the Maximum Vector Length (MVL)?

What is the significance of the Maximum Vector Length (MVL) in vector computers?

How does the strip mining technique improve the efficiency of vector computers?

What is the purpose of computing the corresponding element index i in the CUDA function?

What is the role of the blockIdx.x variable in the CUDA function?

How many threads are launched in the CUDA code in Listing 7.18?

What is the purpose of the daxpy function in the CUDA code?

What is the relationship between the block size and the number of threads per block?

What is the purpose of the grid in the CUDA code?

How many thread blocks are used in the example with 8,192 elements?

What is the purpose of the blockIdx.x variable in the CUDA function?

What is the advantage of using a multithreaded SIMD processor?

What is the purpose of the daxpy function in the CUDA code?

What is the significance of the number 256 in the CUDA code in Listing 7.18?

How does the CUDA function compute the corresponding element index i?

What is the purpose of the grid in the CUDA code?

What is the relationship between the number of threads launched and the number of elements in the vector?

What is the advantage of using a multithreaded SIMD processor in the CUDA code?

How does the CUDA code handle the multiplication and addition operation in the DAXPY function?

What is the role of the block ID in the CUDA function?

What is the significance of the number 512 in the example with 8,192 elements?

What is the purpose of the DAXPY function in the CUDA code?

How does the CUDA code handle the case where the number of elements in the vector is not a multiple of the block size?

Study Notes

Flynn's Taxonomy

SIMD vs MIMD

SIMD Vector Processors

Vector Processor Characteristics

Basic Architecture

Vector Instructions

Flynn's Taxonomy

SIMD vs MIMD

SIMD Vector Processors

Vector Processor Characteristics

Basic Architecture

Vector Instructions

Vector Computers

RV64G vs. RV64V

Vector Instructions Optimizations