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Advanced GPU Customizations

While the previous examples covered fundamental fine-grained GPU optimizations, this document explores additional advanced GPU customization techniques that require direct kernel modifications. These techniques help extract maximum performance from GPU hardware and address specific optimization challenges beyond the basic approaches.

Table of Contents

  1. Thread Divergence Mitigation
  2. Register Usage Optimization
  3. Mixed Precision Computation
  4. Persistent Threads for Load Balancing
  5. Warp Specialization Patterns
  6. Implementation Considerations
  7. References

Thread Divergence Mitigation

GPU performance relies heavily on executing the same instructions across threads in a warp (a group of 32 threads that execute in SIMT fashion). When threads within a warp take different execution paths due to conditional branching, performance suffers significantly.

The Problem

When threads in a warp diverge, the hardware must serialize execution, drastically reducing performance:

// High divergence based on thread ID (problematic)
if (threadIdx.x % 2 == 0) {
    // Even threads take expensive path
    for (int i = 0; i < 100; i++) {
        result = sinf(result) * cosf(result) + 0.1f;
    }
} else {
    // Odd threads take simple path
    result = input[idx] * 2.0f;
}

In this code, every alternate thread within a warp takes a different path, forcing serialized execution.

The Solution

Restructure code to ensure threads in the same warp take the same execution path:

// Low divergence based on block ID (better)
if (blockIdx.x % 2 == 0) {
    // All threads in even-indexed blocks take expensive path
    for (int i = 0; i < 100; i++) {
        result = sinf(result) * cosf(result) + 0.1f;
    }
} else {
    // All threads in odd-indexed blocks take simple path
    result = value * 2.0f;
}

This approach ensures entire warps take the same path, eliminating intra-warp divergence.

Best Practices

  1. Organize your data to minimize divergence (sort by similar processing needs)
  2. Move conditionals to higher levels (block level instead of thread level)
  3. Consider predication for short divergent sections
  4. Restructure algorithms to avoid divergent paths
  5. Use warp-level voting functions to make uniform decisions

Register Usage Optimization

Registers are the fastest memory on the GPU, but they are a limited resource. High register usage per thread can limit occupancy (the number of warps that can run concurrently on a multiprocessor).

The Problem

Using too many variables within a kernel can increase register pressure:

// High register usage
float a1 = input[idx];
float a2 = a1 * 1.1f;
float a3 = a2 * 1.2f;
// ... many more variables
float a16 = a15 * 2.5f;

// Complex computation using many variables
for (int i = 0; i < 20; i++) {
    a1 = a1 + a2 * cosf(a3);
    a2 = a2 + a3 * sinf(a4);
    // ... and so on for many variables
}

This approach consumes many registers per thread, limiting the number of warps that can run simultaneously.

The Solution

Reduce register count by reusing variables and restructuring computations:

// Optimized register usage
float result = input[idx];
float temp = result * 1.1f;

// Replace multiple variables with loop iterations
for (int i = 0; i < 20; i++) {
    // Reuse the same variables in computations
    result = result + temp * cosf(result);
    temp = temp + result * sinf(temp);
    // ...
}

Register Optimization Techniques

  1. Reuse variables rather than creating new ones
  2. Use loop unrolling judiciously to balance between parallelism and register pressure
  3. Consider __launch_bounds__ to control maximum registers per thread
  4. Analyze PTX output to identify register usage
  5. Trade computation for registers when appropriate

Mixed Precision Computation

Modern GPUs support various precision formats, from 64-bit double precision to 16-bit half precision. Leveraging lower precision where appropriate can significantly increase computational throughput.

The Technique

Perform computations in lower precision while maintaining accuracy by using higher precision for critical operations:

// Convert to half precision for computation
half x_f16 = __float2half(x_f32);

// Compute in FP16
half i_f16 = __float2half(i * 0.01f);
half mult = __hmul(x_f16, i_f16);

// Convert back to FP32 for accuracy-sensitive operations
float sin_val = sinf(__half2float(mult));

// Accumulate in FP32 for better precision
result += sin_val;

Benefits of Mixed Precision

  1. Higher computational throughput - many GPUs offer 2-8x more throughput for FP16 vs FP32
  2. Reduced memory bandwidth requirements - smaller data types need less bandwidth
  3. Lower memory footprint - more data fits in caches
  4. Tensor core utilization - specialized hardware for mixed precision computation on newer GPUs

Best Practices

  1. Accumulate in higher precision to prevent error accumulation
  2. Use lower precision for bulk computation where accuracy is less critical
  3. Analyze numerical stability of your algorithm to determine safe precision levels
  4. Consider scale factors to maintain dynamic range in lower precision formats
  5. Test accuracy against full-precision baseline

Persistent Threads for Load Balancing

Traditional CUDA programming assigns a fixed workload to each thread. For workloads with variable execution time, this can lead to load imbalance and idle threads.

The Problem

With traditional work assignment, threads with light workloads finish early and remain idle:

// Traditional approach - fixed work assignment
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
    int work_amount = workloads[idx];  // Variable workloads
    // Do work...
}

The Solution: Persistent Threads

Keep threads alive and let them dynamically grab new work items from a queue:

// Persistent threads approach
while (true) {
    // Atomically grab next work item
    int work_idx = atomicAdd(&queue->head, 1);

    // Check if we've processed all items
    if (work_idx >= size) break;

    // Process the work item
    int work_amount = workloads[work_idx];
    // Do work...
}

Benefits

  1. Improved load balancing - threads that finish early get more work
  2. Higher hardware utilization - less idle time for processing units
  3. Better scaling for irregular workloads
  4. Lower thread management overhead - fewer thread launches
  5. More predictable performance - less sensitivity to workload distribution

Warp Specialization Patterns

Different computations have different execution characteristics. Warp specialization assigns different tasks to different warps within a thread block.

The Technique

Identify warp ID and assign specialized tasks based on it:

int warpId = threadIdx.x / WARP_SIZE;

// Specialize warps for different tasks
if (warpId == 0) {
    // First warp: Trigonometric computations
    for (int i = 0; i < 50; i++) {
        result += sinf(value * i * 0.01f);
    }
} else if (warpId == 1) {
    // Second warp: Polynomial computations
    float x = value;
    float x2 = x * x;
    result = 1.0f + x + x2/2.0f + x3/6.0f + x4/24.0f;
} 
// Other warps get different tasks...

Benefits

  1. Cache utilization - specialized warps might use different cache lines
  2. Instruction cache optimization - fewer total instructions per warp
  3. Reduced divergence - specialized code paths have less branching
  4. Pipeline efficiency - specialized tasks may utilize different execution units
  5. Memory access pattern optimization - different warps can use different memory patterns

Applications

  1. Task-parallel algorithms with distinct phases
  2. Producer-consumer patterns - some warps produce data, others consume it
  3. Cooperative processing - divide complex algorithms into specialized subtasks
  4. Heterogeneous workloads - compute-bound vs memory-bound tasks

Implementation Considerations

When implementing these advanced customization techniques, consider:

  1. Measure impact - Customize based on profiling data, not assumptions
  2. GPU architecture differences - Different generations may respond differently to optimizations
  3. Balance complexity vs. maintainability - Advanced techniques can make code harder to understand
  4. Test across problem sizes - Performance characteristics can change with input scale
  5. Consider portability - Some techniques may not work well across all hardware

References

  1. NVIDIA CUDA C++ Programming Guide: https://docs.nvidia.com/cuda/cuda-c-programming-guide/
  2. NVIDIA CUDA C++ Best Practices Guide: https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/
  3. Volkov, V. (2010). "Better performance at lower occupancy." GPU Technology Conference.
  4. Harris, M. "CUDA Pro Tip: Write Flexible Kernels with Grid-Stride Loops." NVIDIA Developer Blog.
  5. Micikevicius, P. "Achieving Maximum Performance with CUDA Kernels." GTC 2015.
  6. Jia, Z., et al. (2019). "Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking." arXiv:1804.06826.
  7. NVIDIA Parallel Thread Execution ISA: https://docs.nvidia.com/cuda/parallel-thread-execution/

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