Performance Guidelines#

The AMD HIP Performance Guidelines are a set of best practices designed to help developers optimize the performance of AMD GPUs. They cover established parallelization and optimization techniques, coding metaphors, and idioms that can greatly simplify programming for HIP-capable GPU architectures.

By following four main cornerstones, we can exploit the performance optimization potential of HIP.

  • parallel execution

  • memory usage optimization

  • optimization for maximum throughput

  • minimizing memory thrashing

In the following chapters, we will show you their benefits and how to use them effectively.

Parallel execution#

For optimal use, the application should reveal and efficiently imply as much parallelism as possible to keep all system components active.

Application level#

The application should optimize parallel execution across the host and devices using asynchronous calls and streams. Workloads should be assigned based on efficiency: serial to the host, parallel to the devices.

For parallel workloads, when threads need to synchronize to share data, if they belong to the same block, they should use __syncthreads() (see: Synchronization functions) within the same kernel invocation. If they belong to different blocks, they must use global memory with two separate kernel invocations. The latter should be minimized as it adds overhead.

Device level#

Device-level optimization primarily involves maximizing parallel execution across the multiprocessors of the device. This can be achieved by executing multiple kernels concurrently on a device. The management of these kernels is facilitated by streams, which allow for the overlapping of computation and data transfers, enhancing performance. The aim is to keep all multiprocessors busy by executing enough kernels concurrently. However, launching too many kernels can lead to resource contention, so a balance must be found for optimal performance. This approach helps in achieving maximum utilization of the resources of the device.

Multiprocessor level#

Multiprocessor-level optimization involves maximizing parallel execution within each multiprocessor on a device. Each multiprocessor can execute a number of threads concurrently, and the total number of threads that can run in parallel is determined by the number of concurrent threads each multiprocessor can handle.

The key to multiprocessor-level optimization is to efficiently utilize the various functional units within a multiprocessor. This can be achieved by ensuring a sufficient number of resident warps, as at every instruction issue time, a warp scheduler selects an instruction that is ready to execute. This instruction can be another independent instruction of the same warp, exploiting Optimization for maximum instruction throughput, or more commonly an instruction of another warp, exploiting thread-level parallelism.

In comparison, device-level optimization focuses on the device as a whole, aiming to keep all multiprocessors busy by executing enough kernels concurrently. Both levels of optimization are crucial for achieving maximum performance. They work together to ensure efficient utilization of the resources of the GPU, from the individual multiprocessors to the device as a whole.

Memory optimization#

The first step in maximizing memory throughput is to minimize low-bandwidth data transfers. This involves reducing data transfers between the host and the device, as these have lower bandwidth than transfers between global memory and the device.

Additionally, data transfers between global memory and the device should be minimized by maximizing the use of on-chip memory: shared memory and caches. Shared memory acts as a user-managed cache, where the application explicitly allocates and accesses it. A common programming pattern is to stage data from device memory into shared memory. This involves each thread of a block loading data from device memory to shared memory, synchronizing with all other threads of the block, processing the data in shared memory, synchronizing again if necessary, and writing the results back to device global memory.

For some applications, a traditional hardware-managed cache is more appropriate to exploit data locality. On devices of certain compute capabilities, the same on-chip memory is used for both L1 and shared memory, and the amount dedicated to each is configurable for each kernel call.

Finally, the throughput of memory accesses by a kernel can vary significantly depending on the access pattern for each type of memory. Therefore, the next step in maximizing memory throughput is to organize memory accesses as optimally as possible. This is especially important for global memory accesses, as global memory bandwidth is low compared to available on-chip bandwidths and arithmetic instruction throughput. Thus, non-optimal global memory accesses generally have a high impact on performance.

Data Transfer#

Applications should aim to minimize data transfers between the host and the device. This can be achieved by moving more computations from the host to the device, even if it means running kernels that do not fully utilize the parallelism for device. Intermediate data structures can be created, used, and discarded in device memory without being mapped or copied to host memory.

Batching small transfers into a single large transfer can improve performance due to the overhead associated with each transfer. On systems with a front-side bus, using page-locked host memory can enhance data transfer performance.

When using mapped page-locked memory, there is no need to allocate device memory or explicitly copy data between device and host memory. Data transfers occur implicitly each time the kernel accesses the mapped memory. For optimal performance, these memory accesses should be coalesced, similar to global memory accesses.

On integrated systems where device and host memory are physically the same, any copy operation between host and device memory is unnecessary, and mapped page-locked memory should be used instead. Applications can check if a device is integrated by querying the integrated device property.

Device Memory Access#

Memory access instructions may be repeated due to the spread of memory addresses across warp threads. The impact on throughput varies with memory type and is generally reduced when addresses are more scattered, especially in global memory.

Device memory is accessed via 32-, 64-, or 128-byte transactions that must be naturally aligned. Maximizing memory throughput involves coalescing memory accesses of threads within a warp into minimal transactions, following optimal access patterns, using properly sized and aligned data types, and padding data when necessary.

Global memory instructions support reading or writing data of specific sizes (1, 2, 4, 8, or 16 bytes) that are naturally aligned. If the size and alignment requirements are not met, it leads to multiple instructions, reducing performance. Therefore, using data types that meet these requirements, ensuring alignment for structures, and maintaining alignment for all values or arrays is crucial for correct results and optimal performance.

Threads often access 2D arrays at an address calculated as BaseAddress + xIndex + width * yIndex. For efficient memory access, the array and thread block widths should be multiples of the warp size. If the array width is not a multiple of the warp size, it is usually more efficient to allocate it with a width rounded up to the nearest multiple and pad the rows accordingly.

Local memory is used for certain automatic variables, such as arrays with non-constant indices, large structures or arrays, and any variable when the kernel uses more registers than available. Local memory resides in device memory, leading to high latency and low bandwidth similar to global memory accesses. However, it is organized for consecutive 32-bit words to be accessed by consecutive thread IDs, allowing full coalescing when all threads in a warp access the same relative address.

Shared memory, located on-chip, provides higher bandwidth and lower latency than local or global memory. It is divided into banks that can be simultaneously accessed, boosting bandwidth. However, bank conflicts, where two addresses fall in the same bank, lead to serialized access and decreased throughput. Therefore, understanding how memory addresses map to banks and scheduling requests to minimize conflicts is crucial for optimal performance.

Constant memory is in device memory and cached in the constant cache. Requests are split based on different memory addresses, affecting throughput, and are serviced at the throughput of the constant cache for cache hits, or the throughput of the device memory otherwise.

Texture and surface memory are stored in device memory and cached in texture cache. This setup optimizes 2D spatial locality, leading to better performance for threads reading close 2D addresses. Reading device memory through texture or surface fetching can be advantageous, offering higher bandwidth for local texture fetches or surface reads, offloading addressing calculations, allowing data broadcasting, and optional conversion of 8-bit and 16-bit integer input data to 32-bit floating-point values on-the-fly.

Optimization for maximum instruction throughput#

To maximize instruction throughput:

  • minimize low throughput arithmetic instructions

  • minimize divergent warps inflicted by control flow instructions

  • minimize the number of instruction as possible

  • maximize instruction parallelism

Arithmetic instructions#

The type and complexity of arithmetic operations can significantly impact the performance of your application. We are highlighting some hints how to maximize it.

Using efficient operations: Some arithmetic operations are more costly than others. For example, multiplication is typically faster than division, and integer operations are usually faster than floating-point operations, especially with double-precision.

Minimizing low-throughput instructions: This might involve trading precision for speed when it does not affect the final result. For instance, consider using single-precision arithmetic instead of double-precision.

Leverage intrinsic functions: Intrinsic functions are pre-defined functions available in HIP that can often be executed faster than equivalent arithmetic operations (subject to some input or accuracy restrictions). They can help optimize performance by replacing more complex arithmetic operations.

Avoiding divergent warps: Divergent warps occur when threads within the same warp follow different execution paths. This can happen due to conditional statements that lead to different arithmetic operations being performed by different threads. Divergent warps can significantly reduce instruction throughput, so try to structure your code to minimize divergence.

Optimizing memory access: The efficiency of memory access can impact the speed of arithmetic operations. Coalesced memory access, where threads in a warp access consecutive memory locations, can improve memory throughput and thus the speed of arithmetic operations.

Maximizing instruction parallelism: Some GPU architectures could issue parallel independent instructions simultaneously, for example integer and floating point, or two operations with independent inputs and outputs. Mostly this is a work for compiler, but expressing parallelism in the code explicitly can improve instructions throughput.

Control flow instructions#

Flow control instructions (if, else, for, do, while, break, continue, switch) can impact instruction throughput by causing threads within a warp to diverge and follow different execution paths. To optimize performance, control conditions should be written to minimize divergent warps. For example, when the control condition depends on (threadIdx / warpSize), no warp diverges. The compiler may optimize loops or short if or switch blocks using branch predication, preventing warp divergence. With branch predication, instructions associated with a false predicate are scheduled but not executed, avoiding unnecessary operations.


Synchronization ensures that all threads within a block have completed their computations and memory accesses before moving forward, which is critical when threads are dependent on the results of other threads. However, synchronization can also lead to performance overhead, as it requires threads to wait, potentially leading to idle GPU resources.

__syncthreads() is used to synchronize all threads in a block, ensuring that all threads have reached the same point in the code and that shared memory is visible to all threads after the point of synchronization.

An alternative way to synchronize is using streams. Different streams can execute commands out of order with respect to one another or concurrently. This allows for more fine-grained control over the execution order of commands, which can be beneficial in certain scenarios.

Minimizing memory thrashing#

Applications frequently allocating and freeing memory may experience slower allocation calls over time. This is expected as memory is released back to the operating system. To optimize performance in such scenarios, consider some recommendations:

  • avoid allocating all available memory with hipMalloc / hipHostMalloc, as this immediately reserves memory and can block other applications from using it. This could strain the operating system schedulers or even prevent other applications from running on the same GPU.

  • aim to allocate memory in suitably sized blocks early in the lifecycle of the application and deallocate only when the application no longer needs it. Minimize the number of hipMalloc and hipFree calls in your application, particularly in areas critical to performance.

  • if an application is unable to allocate sufficient device memory, consider resorting to other memory types such as hipHostMalloc or hipMallocManaged. While these may not offer the same performance, they can allow the application to continue running.

  • For supported platforms, hipMallocManaged allows for oversubscription. With the right memory advise policies, it can maintain most, if not all, of the performance of hipMalloc. hipMallocManaged does not require an allocation to be resident until it is needed or prefetched, easing the load on the operating system schedulers and facilitating multi-tenant scenarios.