Understanding CUDA Thread and Block Patterns: A Visual AnalysisYep!

TL;DR

Thread and block configurations significantly impact CUDA kernel performance
Power-of-2 sizes provide most efficient execution patterns
Warp alignment (multiples of 32 threads) optimizes memory coalescing
Block size involves trade-offs between thread cooperation and SM distribution
Visual analysis helps understand how different configurations affect execution

Interactive Exploration

Before diving into the technical details, try out our interactive CUDA visualizer to get a hands-on understanding of thread and block patterns:

Block Visualizer

CUDA Architecture Overview

Before diving into configurations, let’s understand key CUDA concepts:

Thread Hierarchy

Grid
├── Block 0
│   ├── Thread (0,0)
│   ├── Thread (0,1)
│   └── ...
├── Block 1
│   ├── Thread (1,0)
│   └── ...
└── ...

Threads: Basic execution units
Warps: Groups of 32 threads executed simultaneously
Blocks: Collections of threads that can cooperate
Grid: Collection of blocks executing the same kernel

Hardware Constraints

Maximum threads per block: 1024
Warp size: 32 threads
Typical SM can handle multiple blocks simultaneously
Memory access is coalesced within warps

CUDA Architecture Deep Dive

Hardware Architecture (SM89)

Streaming Multiprocessor (SM)
├── Warp Schedulers: 4 per SM
├── CUDA Cores: 128 per SM
├── Shared Memory: 64KB per SM
├── L1 Cache: 128KB per SM
└── Register File: 64K 32-bit registers

Memory Hierarchy

Device Memory (Global Memory)
├── L2 Cache (Shared by all SMs)
│   └── Cache Line Size: 128 bytes
├── L1 Cache (Per SM)
│   └── Cache Line Size: 128 bytes
├── Shared Memory (Per SM)
│   └── Bank Width: 32-bit
└── Register File (Per SM)
    └── Access Latency: 1 cycle

Theoretical Performance Metrics

Global Memory Bandwidth: 912 GB/s
Shared Memory Bandwidth: ~19 TB/s per SM
Register Bandwidth: ~39 TB/s per SM
Warp Scheduling Rate: 1 instruction per clock

Experimental Setup

Our test kernel uses unified memory and records detailed execution patterns:

// Structure to store thread processing info
struct ThreadInfo {
    int index;      // Global array index
    int blockId;    // Block identifier
    int threadId;   // Thread identifier within block
    float value;    // Computed value
};
 
__global__
void add(int n, float *x, float *y, ThreadInfo *info) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        y[idx] += x[idx];
        // Record thread/block information
        info[idx].index = idx;
        info[idx].blockId = blockIdx.x;
        info[idx].threadId = threadIdx.x;
        info[idx].value = y[idx];
    }
}

1. Power-of-2 Configurations

Standard Case (N=256, Block=32)

// Launch configuration
int N = 256;
int blockSize = 32;  // One warp
int numBlocks = (N + blockSize - 1) / blockSize;  // 8 blocks
add<<<numBlocks, blockSize>>>(N, x, y, info);

Technical Analysis:

Block size matches warp size (32 threads)
Perfect memory coalescing within warps
8 blocks distribute evenly across SMs
100% thread utilization: 256/(32*8) = 1.0

Performance Implications:

Optimal warp execution efficiency
No partial warps
Good SM occupancy
Minimal scheduling overhead

Large Blocks (N=1024, Block=256)

// 256 threads per block = 8 warps per block
add<<<4, 256>>>(1024, x, y, info);

Technical Analysis:

8 warps per block (256/32)
4 blocks total
Higher register pressure per block
More thread cooperation possible within blocks

Performance Considerations:

May limit SM occupancy due to resource usage
Better for compute-bound kernels with thread cooperation
Reduced block scheduling overhead

2. Small Block Configurations

Minimal Blocks (N=100, Block=8)

// Sub-warp block size
add<<<13, 8>>>(100, x, y, info);

Technical Deep Dive:

Block size (8) is 1/4 of a warp
Each warp underutilized (75% idle threads)
13 blocks for 100 elements
Thread utilization: 100/(8*13) ≈ 96.2%

Performance Impact:

Poor warp execution efficiency
Higher block scheduling overhead
Better load balancing across SMs
Worse memory coalescing

3. Prime Numbers and Odd Sizes

Prime Array Size (N=97, Block=32)

// Prime number of elements with warp-sized blocks
add<<<4, 32>>>(97, x, y, info);

Warp-Level Analysis:

Full warps except last block
Last block: 97 - (3*32) = 1 element
Last warp: 31/32 threads idle
Overall thread utilization: 97/(32*4) ≈ 75.8%

Memory Access Patterns:

First 3 blocks: Coalesced access
Last block: Highly divergent
Potential for bank conflicts

4. Memory Access Pattern Visualizations

Coalesced vs Non-Coalesced Access

Coalesced Access (Efficient):
Thread   0   1   2   3  ...  31    // One warp
Memory  [0] [1] [2] [3] ... [31]   // One transaction
         └───────────128B───────┘

Non-Coalesced Access (Inefficient):
Thread   0   1   2   3  ...  31    // One warp
Memory  [0] [32][64][96]... [992]  // Multiple transactions
         ↑   ↑   ↑   ↑      ↑
         └───┴───┴───┴──...─┘
         32 separate transactions

Memory Bank Access Patterns

Shared Memory Banks (32 Banks):
Bank0  Bank1  Bank2  Bank3 ... Bank31
[0]    [1]    [2]    [3]  ... [31]
[32]   [33]   [34]   [35] ... [63]
[64]   [65]   [66]   [67] ... [95]

Sequential Access (No Conflicts):
Thread0 → Bank0  [0]
Thread1 → Bank1  [1]
Thread2 → Bank2  [2]
...
Thread31→ Bank31 [31]

Strided Access (2-way Bank Conflicts):
Thread0 → Bank0  [0]
Thread1 → Bank0  [32] ⚠️ Conflict!
Thread2 → Bank1  [2]
Thread3 → Bank1  [34] ⚠️ Conflict!

Performance Optimization Guidelines

Warp Alignment

// Prefer warp-aligned block sizes
blockSize = 32 * N; // Where N is 1, 2, 4, 8

Occupancy Optimization
- Balance block size vs. number of blocks
- Consider register usage
- Account for shared memory requirements

Memory Access Patterns

// Ensure coalesced access within warps
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Access pattern follows thread index
y[idx] += x[idx];

Block Size Selection

// Rule of thumb for compute-bound kernels
const int threadsPerBlock = 256;  // 8 warps
// Rule of thumb for memory-bound kernels
const int threadsPerBlock = 128;  // 4 warps

Hands-On Learning

To solidify your understanding of these concepts, experiment with our interactive visualizer:

→ Try Different Configurations

Observe how different grid and block sizes affect:

Thread utilization efficiency
Memory coalescing potential
Warp-level execution patterns

Conclusions

This analysis reveals several key insights for CUDA optimization:

Block Size Selection
- Match warp size (32) or multiples
- Consider resource limits
- Balance with total thread count
Thread Utilization
- Power-of-2 sizes optimize efficiency
- Handle irregular sizes carefully
- Consider warp-level effects
Performance Trade-offs
- Block size affects resource usage
- Thread count impacts memory patterns
- Configuration affects SM utilization

These insights help in selecting optimal configurations for different CUDA workloads, balancing factors like:

Memory access patterns
Thread cooperation needs
SM utilization
Scheduling overhead

What’s Next?

In upcoming articles, I’ll expand on:

Dynamic parallelism strategies
Tensor Core utilization for AI inference
Memory throughput optimization techniques
Register pressure vs. occupancy trade-offs

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Understanding CUDA Thread and Block Patterns: A Visual Analysis