Day 86: Occupancy vs. ILP
Optimizing CUDA kernels involves balancing two key performance factors: occupancy and Instruction-Level Parallelism (ILP). High occupancy ensures that many warps are active on an SM, which helps hide memory latency and keep the hardware busy. In contrast, ILP focuses on increasing the number of instructions executed per thread by unrolling loops and reordering instructions, which can lead to more efficient use of the arithmetic units. However, forcing ILP often increases register usage per thread, which can reduce occupancy if fewer threads can be scheduled concurrently. In this lesson, we explore the trade-offs between occupancy and ILP, and discuss strategies for finding the right balance according to the CUDA C Best Practices Guide.
Table of Contents
- Overview
- Defining Occupancy and ILP
- The Trade-Off: How ILP Affects Occupancy
- Guidelines from the CUDA C Best Practices Guide
- Code Example: Unrolling vs. Occupancy
- Conceptual Diagrams
- Conclusion & Next Steps
1. Overview
Kernel performance in CUDA is influenced by both how many threads are active (occupancy) and how efficiently each thread executes its instructions (ILP). While high occupancy can hide latencies by switching between warps, increasing ILP can reduce the total number of instructions executed by a thread, thereby potentially improving throughput. The challenge lies in the fact that optimizing for ILP (for example, by unrolling loops) may increase register usage per thread and, in turn, reduce occupancy.
2. Defining Occupancy and ILP
Occupancy: The ratio of active warps per Streaming Multiprocessor (SM) to the maximum number of warps that can be supported. High occupancy allows the GPU to hide latency effectively by scheduling other warps when one is waiting for data.
Instruction-Level Parallelism (ILP): The extent to which instructions within a single thread can be executed in parallel. Techniques like loop unrolling and instruction reordering can expose more ILP, reducing the overall instruction count and improving the throughput per thread.
3. The Trade-Off: How ILP Affects Occupancy
When you unroll loops or otherwise restructure code to increase ILP:
- Increased Register Usage: More unrolling typically requires more registers per thread to hold intermediate values.
- Lower Occupancy: If register usage per thread increases, the number of threads that can be scheduled concurrently on an SM may drop, reducing occupancy.
- Performance Impact: The overall performance depends on the balance. If the improved ILP leads to significantly fewer cycles per thread, it might compensate for lower occupancy. However, if register spilling occurs or occupancy falls too low, overall throughput may suffer.
4. Guidelines from the CUDA C Best Practices Guide
- Profile First: Use tools like Nsight Compute to determine if your kernel is compute-bound or memory-bound.
- Adjust Unrolling Cautiously: Unroll loops to increase ILP, but monitor register usage to avoid excessive spills.
- Balance Occupancy and ILP: Aim for a sweet spot where the kernel has enough ILP for efficient computation without severely reducing occupancy.
- Iterative Tuning: Experiment with different levels of unrolling and use compiler flags (e.g.,
-maxrregcount) to guide resource allocation.
5. Code Example: Unrolling vs. Occupancy
Below is a simplified kernel that demonstrates loop unrolling to increase ILP. Compare this with a version that uses a standard loop to see how unrolling affects register usage and occupancy.
Standard Version
__global__ void standardKernel(float* data, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float sum = 0.0f;
for (int i = 0; i < N; i++) {
sum += data[i] * 0.5f;
}
data[idx] = sum;
}Unrolled Version (Increases ILP)
__global__ void unrolledKernel(float* data, int N) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float sum = 0.0f;
// Unroll loop by factor of 4
for (int i = 0; i < N; i += 4) {
sum += data[i] * 0.5f;
sum += data[i+1] * 0.5f;
sum += data[i+2] * 0.5f;
sum += data[i+3] * 0.5f;
}
data[idx] = sum;
}Note: The unrolled version may use more registers to hold intermediate results. Profiling with Nsight Compute will help decide which version yields better overall throughput.
6. Conceptual Diagrams
Diagram 1: Trade-Off Overview
flowchart TD
A[Increase ILP (e.g., loop unrolling)]
B[Higher register usage per thread]
C[Lower occupancy on SMs]
D[Potential performance gain due to reduced instruction count]
E[Overall kernel performance]
A --> B
B --> C
A --> D
D --> E
C --> EExplanation:
This diagram shows that increasing ILP leads to higher register usage, which may lower occupancy. The final performance depends on whether the reduced instruction count outweighs the occupancy loss.
Diagram 2: Impact of Loop Unrolling on Registers and Occupancy
flowchart LR
A[Standard Loop]
B[Lower ILP, moderate register usage]
C[High Occupancy]
D[Loop Unrolling]
E[Increased ILP, higher register usage]
F[Reduced Occupancy (if register spill occurs)]
A --> B
B --> C
D --> E
E --> FExplanation:
Standard loops tend to use fewer registers, preserving occupancy. In contrast, unrolled loops improve ILP but can lead to increased register usage, possibly reducing occupancy.
Diagram 3: Balancing ILP and Occupancy
flowchart TD
A[Kernel Design]
B[Apply ILP Techniques (e.g., unrolling)]
C[Profile register usage and occupancy]
D[Adjust unrolling factor and use -maxrregcount if needed]
E[Optimal Balance Achieved]
A --> B
B --> C
C --> D
D --> EExplanation:
This iterative feedback loop illustrates the process of balancing ILP and occupancy. The designer applies ILP techniques, profiles the kernel, adjusts parameters, and iterates until the optimal balance is reached.
7. Conclusion & Next Steps
Balancing occupancy and ILP is a critical challenge in CUDA optimization. While increasing ILP through techniques like loop unrolling can reduce the total instruction count, it may also increase register usage and lower occupancy. The key is to profile and tune iteratively, using tools like Nsight Compute to achieve the best overall throughput.
Next Steps:
- Profile Your Kernels: Use Nsight Compute to gather detailed register and occupancy metrics.
- Experiment: Try different unrolling factors and compiler flags to see their impact.
- Iterate: Use the feedback loop to fine-tune the balance between ILP and occupancy.
- Document: Keep detailed records of your tuning decisions to help future optimization efforts.
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