Chapter 6: GPU Performance Metrics and Analysis

Analyzing and optimizing the performance of GPU applications is crucial for achieving high efficiency and utilization of GPU hardware resources. In this chapter, we will explore key GPU performance metrics, profiling and optimization tools, techniques for identifying performance bottlenecks, and strategies for improving GPU performance.

Throughput, Latency, and Memory Bandwidth

Three fundamental metrics for evaluating GPU performance are throughput, latency, and memory bandwidth. Understanding these metrics and their implications is essential for analyzing and optimizing GPU applications.

Throughput

Throughput refers to the number of operations or tasks that a GPU can complete in a given amount of time. It is typically measured in floating-point operations per second (FLOPS) or instructions per second (IPS). GPUs are designed to achieve high throughput by exploiting parallelism and executing a large number of threads concurrently.

The theoretical peak throughput of a GPU can be calculated using the following formula:

Peak Throughput (FLOPS) = Number of CUDA Cores × Clock Frequency × FLOPS per CUDA Core per Cycle

For example, an NVIDIA GeForce RTX 2080 Ti GPU has 4352 CUDA cores, a base clock frequency of 1350 MHz, and each CUDA core can perform 2 floating-point operations per cycle (FMA - Fused Multiply-Add). Therefore, its theoretical peak throughput is:

Peak Throughput (FLOPS) = 4352 × 1350 MHz × 2 = 11.75 TFLOPS

However, achieving the theoretical peak throughput in practice is challenging due to various factors such as memory access patterns, branch divergence, and resource constraints.

Latency

Latency refers to the time it takes for a single operation or task to complete. In the context of GPUs, latency is often associated with memory access operations. GPUs have a hierarchical memory system, and accessing data from different levels of the memory hierarchy incurs different latencies.

Typical latencies for various memory levels in a GPU are:

Registers: 0-1 cycles
Shared Memory: 1-2 cycles
L1 Cache: 20-30 cycles
L2 Cache: 200-300 cycles
Global Memory (DRAM): 400-800 cycles

Latency can have a significant impact on GPU performance, especially when there are dependencies between operations or when threads are waiting for data to be fetched from memory. Techniques such as latency hiding, prefetching, and caching can help mitigate the impact of latency on GPU performance.

Memory Bandwidth

Memory bandwidth refers to the rate at which data can be transferred between the GPU and its memory subsystem. It is typically measured in bytes per second (B/s) or gigabytes per second (GB/s). GPUs have high-bandwidth memory interfaces, such as GDDR6 or HBM2, to support the data-intensive nature of graphics and compute workloads.

The theoretical peak memory bandwidth of a GPU can be calculated using the following formula:

Peak Memory Bandwidth (GB/s) = Memory Clock Frequency × Memory Bus Width ÷ 8

For example, an NVIDIA GeForce RTX 2080 Ti GPU has a memory clock frequency of 7000 MHz (effective) and a memory bus width of 352 bits. Therefore, its theoretical peak memory bandwidth is:

Peak Memory Bandwidth (GB/s) = 7000 MHz × 352 bits ÷ 8 = 616 GB/s

Memory bandwidth is a critical factor in GPU performance, as many GPU applications are memory-bound, meaning their performance is limited by the rate at which data can be transferred between the GPU and memory. Optimizing memory access patterns, minimizing data transfers, and leveraging memory hierarchy can help improve memory bandwidth utilization.

Profiling and Performance Optimization Tools

Profiling and performance optimization tools are essential for analyzing GPU application behavior, identifying performance bottlenecks, and guiding optimization efforts. These tools provide insights into various aspects of GPU performance, such as kernel execution time, memory access patterns, occupancy, and resource utilization.

Some popular profiling and performance optimization tools for GPUs include:

NVIDIA Visual Profiler (nvvp): A graphical profiling tool that provides a comprehensive view of GPU application performance. It allows developers to analyze kernel execution, memory transfers, and API calls, and provides recommendations for optimization.
NVIDIA Nsight: An integrated development environment (IDE) that includes profiling and debugging capabilities for GPU applications. It supports various programming languages and frameworks, such as CUDA, OpenCL, and OpenACC.
NVIDIA Nsight Compute: A standalone profiling tool that focuses on GPU kernel performance analysis. It provides detailed performance metrics, such as instruction throughput, memory efficiency, and occupancy, and helps identify performance bottlenecks at the source code level.
AMD Radeon GPU Profiler (RGP): A profiling tool for AMD GPUs that captures and visualizes performance data for DirectX, Vulkan, and OpenCL applications. It provides insights into GPU utilization, memory usage, and pipeline stalls.
AMD Radeon GPU Analyzer (RGA): A static analysis tool that analyzes GPU shader code and provides performance predictions, resource usage, and optimization suggestions.

These tools typically work by instrumenting the GPU application code, collecting performance data during execution, and presenting the data in a user-friendly format for analysis. They often provide timeline views, performance counters, and source code correlation to help developers identify performance issues and optimize their code.

Example: Profiling a CUDA application using NVIDIA Visual Profiler (nvvp)

Build the CUDA application with profiling enabled:
```
nvcc -o myapp myapp.cu -lineinfo
```
Run the application with profiling:
```
nvprof ./myapp
```
Open the Visual Profiler:
```
nvvp
```
Import the profiling data generated by nvprof.
Analyze the timeline view, kernel performance, memory transfers, and API calls.
Identify performance bottlenecks and optimize the code based on the profiler's recommendations.

Identifying Performance Bottlenecks

Identifying performance bottlenecks is crucial for optimizing GPU applications. Performance bottlenecks can arise from various factors, such as inefficient memory access patterns, low occupancy, branch divergence, and resource constraints. Some common techniques for identifying performance bottlenecks include:

Profiling: Using profiling tools to measure kernel execution time, memory transfer time, and API overhead can help identify which parts of the application are consuming the most time and resources.
Analyzing Occupancy: Occupancy refers to the ratio of active warps to the maximum number of warps supported by a GPU. Low occupancy can indicate underutilization of GPU resources and may suggest the need for optimizing block and grid dimensions or reducing register and shared memory usage.
Examining Memory Access Patterns: Inefficient memory access patterns, such as non-coalesced memory accesses or frequent accesses to global memory, can significantly impact GPU performance. Analyzing memory access patterns using profiling tools can help identify opportunities for optimization, such as using shared memory or improving data locality.
Investigating Branch Divergence: Branch divergence occurs when threads within a warp take different execution paths due to conditional statements. Divergent branches can lead to serialization and reduced performance. Identifying and minimizing branch divergence can help improve GPU performance.
Monitoring Resource Utilization: GPUs have limited resources, such as registers, shared memory, and thread blocks. Monitoring resource utilization using profiling tools can help identify resource bottlenecks and guide optimization efforts, such as reducing register usage or partitioning data to fit in shared memory.

Example: Identifying a memory access bottleneck using NVIDIA Nsight Compute

Profile the CUDA application using Nsight Compute:
```
ncu -o profile.ncu-rep ./myapp
```
Open the generated profile report in Nsight Compute.
Analyze the "Memory Workload Analysis" section to identify inefficient memory access patterns, such as non-coalesced accesses or high global memory usage.
Optimize the memory access patterns based on the insights provided by Nsight Compute, such as using shared memory or improving data locality.

Strategies for Improving GPU Performance

Once performance bottlenecks have been identified, various strategies can be employed to improve GPU performance. Some common optimization strategies include:

Maximizing Parallelism: Ensure that the application is decomposed into a sufficient number of parallel tasks to fully utilize the GPU resources. This may involve adjusting block and grid dimensions, using streams for concurrent execution, or exploiting task-level parallelism.
Optimizing Memory Access Patterns: Improve memory access efficiency by minimizing global memory accesses, using shared memory for frequently accessed data, and ensuring coalesced memory accesses. Techniques such as memory tiling, data layout transformations, and caching can help optimize memory performance.
Reducing Branch Divergence: Minimize branch divergence by restructuring code to avoid divergent branches within a warp. Techniques such as branch predication, data-dependent branching, and warp-level programming can help reduce the impact of branch divergence.
Exploiting Memory Hierarchy: Leverage the GPU memory hierarchy effectively by maximizing the use of registers and shared memory for frequently accessed data. Use texture memory and constant memory for read-only data that exhibits spatial locality or is accessed uniformly across threads.
Overlapping Computation and Memory Transfers: Hide memory transfer latency by overlapping computation with memory transfers using CUDA streams or OpenCL command queues. This allows the GPU to perform computations while data is being transferred between the host and device memory.
Tuning Kernel Launch Parameters: Experiment with different block and grid sizes to find the optimal configuration for each kernel. The optimal launch parameters depend on factors such as the number of registers used per thread, shared memory usage, and the characteristics of the GPU architecture.
Minimizing Host-Device Data Transfers: Reduce the amount of data transferred between the host (CPU) and device (GPU) by performing as much computation as possible on the GPU. Batch small transfers into larger ones to amortize the overhead of each transfer.
Using Asynchronous Operations: Leverage asynchronous operations, such as asynchronous memory copies and kernel launches, to overlap computation and communication. This allows the CPU to perform other tasks while the GPU is executing, improving overall application performance.

Example: Optimizing memory access patterns using shared memory in CUDA

Original code with inefficient global memory accesses:

__global__ void myKernel(float* data, int n) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid < n) {
        float result = 0.0f;
        for (int i = 0; i < n; i++) {
            result += data[tid] * data[i];
        }
        data[tid] = result;
    }
}

Optimized code using shared memory:

__global__ void myKernel(float* data, int n) {
    __shared__ float sharedData[256];
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    int localIdx = threadIdx.x;
 
    if (tid < n) {
        sharedData[localIdx] = data[tid];
    }
    __syncthreads();
 
    if (tid < n) {
        float result = 0.0f;
        for (int i = 0; i < blockDim.x; i++) {
            result += sharedData[localIdx] * sharedData[i];
        }
        data[tid] = result;
    }
}

In the optimized code, the input data is first loaded into shared memory, which has much lower latency compared to global memory. The computation is then performed using the shared memory, reducing the number of global memory accesses and improving performance.

Conclusion

Analyzing and optimizing GPU performance is essential for developing efficient and high-performance GPU applications. By understanding key performance metrics such as throughput, latency, and memory bandwidth, developers can make informed decisions about optimizing their code.

Profiling and performance optimization tools play a crucial role in identifying performance bottlenecks and guiding optimization efforts. These tools provide valuable insights into kernel execution, memory access patterns, occupancy, and resource utilization, enabling developers to focus their optimization efforts on the most critical areas.

Common optimization strategies include maximizing parallelism, optimizing memory access patterns, reducing branch divergence, etc.

Here are some common strategies for optimizing GPU performance, continued in Markdown format:

Reducing Branch Divergence: Divergent control flow within a warp/wavefront can lead to serialization and reduced SIMD efficiency. Algorithms should be structured to minimize branch divergence where possible. Techniques such as branch predication, data-dependent branching, and warp-level programming can help reduce the impact of branch divergence.
Exploiting Memory Hierarchy: Leverage the GPU memory hierarchy effectively by maximizing the use of registers and shared memory for frequently accessed data. Use texture memory and constant memory for read-only data that exhibits spatial locality or is accessed uniformly across threads.
Overlapping Computation and Memory Transfers: Hide memory transfer latency by overlapping computation with memory transfers using CUDA streams or OpenCL command queues. This allows the GPU to perform computations while data is being transferred between the host and device memory.
Tuning Kernel Launch Parameters: Experiment with different block and grid sizes to find the optimal configuration for each kernel. The optimal launch parameters depend on factors such as the number of registers used per thread, shared memory usage, and the characteristics of the GPU architecture.
Minimizing Host-Device Data Transfers: Reduce the amount of data transferred between the host (CPU) and device (GPU) by performing as much computation as possible on the GPU. Batch small transfers into larger ones to amortize the overhead of each transfer.
Using Asynchronous Operations: Leverage asynchronous operations, such as asynchronous memory copies and kernel launches, to overlap computation and communication. This allows the CPU to perform other tasks while the GPU is executing, improving overall application performance.

Example: Optimizing memory access patterns using shared memory in CUDA

Original code with inefficient global memory accesses:

__global__ void myKernel(float* data, int n) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    if (tid < n) {
        float result = 0.0f;
        for (int i = 0; i < n; i++) {
            result += data[tid] * data[i];
        }
        data[tid] = result;
    }
}

Optimized code using shared memory:

__global__ void myKernel(float* data, int n) {
    __shared__ float sharedData[256];
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    int localIdx = threadIdx.x;
 
    if (tid < n) {
        sharedData[localIdx] = data[tid];
    }
    __syncthreads();
 
    if (tid < n) {
        float result = 0.0f;
        for (int i = 0; i < blockDim.x; i++) {
            result += sharedData[localIdx] * sharedData[i];
        }
        data[tid] = result;
    }
}

Chapter 5 Gpu Memory System Design Chapter 7 Streaming Multiprocessor Design