Concurrent Kernel

Concurrent execution requires kernels to be scheduled on independent hardware resources to avoid contention. Key independent units include:

• Separate Streaming Multiprocessors (SMs) or compute clusters
• Dedicated memory controllers and cache hierarchies
• Isolated execution pipelines (e.g., integer vs. floating-point units)

Kernels competing for the same physical resource (e.g., L1 cache, register file) will serialize, negating concurrency benefits. Profiling tools like NVIDIA Nsight Compute or AMD ROCm are essential to verify resource isolation.

Kernels must operate on non-overlapping memory regions to prevent data races and ensure correctness. This involves:

• Distinct input/output buffers in global memory
• Independent constant memory allocations
• Separate shared memory allocations per thread block

Overlapping memory accesses force serialization through hardware memory coherency protocols, creating implicit synchronization points. Techniques like memory aliasing analysis during compilation are used to verify separation.

Concurrency is enabled through asynchronous execution models where kernel launches are non-blocking operations. This is managed via:

• Command Queues (OpenCL) or CUDA Streams that sequence operations
• Host-Device decoupling, allowing CPU execution to continue
• Explicit dependency management using events or stream synchronization

Without proper stream management, kernels default to a single serial queue. The CUDA Graph API provides a higher-level abstraction for defining and launching concurrent workflow graphs.

Effective concurrency requires each kernel to have sufficient thread-level parallelism to keep its assigned hardware resources busy. This is measured by occupancy: the ratio of active warps/wavefronts to the maximum supported per SM.

Low-occupancy kernels waste hardware resources that could be allocated to concurrent kernels. Optimization techniques include:

• Adjusting workgroup size and register usage
• Utilizing prefetching to hide memory latency
• Balancing compute intensity with available parallelism

Modern NPUs use hardware schedulers to dynamically partition resources among concurrent kernels. Key mechanisms include:

• Time-slicing at the warp/wavefront level on shared SMs
• Spatial partitioning of compute units and cache ways
• Quality-of-Service (QoS) policies for priority-based scheduling

This partitioning is often transparent to the programmer but can be influenced through compute preemption APIs or context priority settings to control fairness and latency.

True concurrency requires kernels to form a Directed Acyclic Graph (DAG) with no cyclic dependencies. Dependencies arise from:

• Data flow (output of Kernel A is input to Kernel B)
• Synchronization primitives like barriers or atomic operations
• Shared resource locks (e.g., hardware semaphores)

Tools like NVIDIA Nsight Systems visualize execution timelines to identify hidden serializations. The CUDA Graph and SYCL Graph models explicitly capture these dependencies for optimal scheduling.

A programming model where the host CPU launches a computational kernel without waiting for its completion, enabling overlap of computation and data transfer. This is a prerequisite for true kernel concurrency. Key aspects include:

• Non-blocking launches allow the CPU to queue multiple kernels or memory operations.
• Streams or command queues are used to define sequences of operations that execute in order but asynchronously relative to the host.
• Synchronization primitives (e.g., events, barriers) are required to safely coordinate dependencies between concurrent tasks.

The physical or logical division of accelerator resources (compute units, memory channels) to isolate and execute multiple kernels simultaneously. This prevents resource contention and ensures quality of service. Implementations include:

• Streaming Multiprocessor (SM) partitions on GPUs, where SMs are allocated to specific kernels.
• Time-slicing at a hardware scheduler level, allowing kernels to share resources over very short time intervals.
• Memory bandwidth allocation mechanisms to guarantee minimum throughput for concurrent workloads.

A directed acyclic graph (DAG) that models the data and execution dependencies between kernels. Schedulers use this graph to determine which kernels can run concurrently versus sequentially. Characteristics include:

• Nodes represent kernels or memory copy operations.
• Edges represent dependencies, often enforced by events or semaphores.
• Graph-based launch APIs (e.g., CUDA Graphs, SYCL Graph) allow the entire workflow to be defined and scheduled as a single unit, optimizing concurrency opportunities.

A performance-degrading condition where concurrently executing kernels compete for shared hardware resources, leading to reduced effective throughput. Common points of contention include:

• Memory bandwidth: Multiple kernels reading/writing simultaneously saturate the memory bus.
• Cache space: Kernels evict each other's working sets, increasing cache misses.
• Shared functional units: Competition for special units like tensor cores or ray-tracing cores. Profiling tools measure contention via performance counters like L2 cache throughput and DRAM bandwidth utilization.

A classification critical for effective concurrent scheduling. Mixing kernels with complementary resource demands maximizes overall system throughput.

• Compute-bound kernels are limited by ALU/tensor core speed. They can often run concurrently with memory-bound kernels, which are limited by memory bandwidth.
• Memory-bound kernels spend significant time waiting for data. Running two memory-bound kernels concurrently typically leads to high contention and diminished returns.
• The roofline model is an analytical tool used to visually classify kernels and estimate the benefit of concurrent execution.

The use of multiple independent command queues (streams) to expose concurrency opportunities to the hardware scheduler. Kernels in different streams can execute out-of-order and concurrently, while kernels within a stream execute sequentially.

• Default stream forces serialization.
• Explicit stream management is required for concurrency.
• Priority streams allow high-priority kernels (e.g., latency-sensitive) to preempt others. This is a software mechanism to express parallelism that the hardware scheduler can then map to available resources.