# Prime Time Tactics--Sieve Tweaks and Boosters
## 3 Parallel Sieving Strategies
### Generic Considerations
Prominent options include CUDA (perhaps the most widely used framework today, but limited to NVIDIA hardware), Metal (specific to Apple devices), Vulkan (a newer contender in the GPGPU area, though not yet widely adopted), and OpenCL. OpenCL is a mature platform currently at version 3, supported by major vendors such as NVIDIA, AMD/Xilinx, and Intel, as well as many smaller players. OpenCL also targets devices like FPGAs and various niche hardware from companies like Qualcomm, Samsung, and Texas Instruments. Its practical utility is significant, given that an OpenCL program can run on any OpenCL platform, including most CPUs. Due to its extensive support and versatility, OpenCL is particularly useful. Many professionals are already familiar with CUDA, so this approach will not be used here; instead, GPGPU algorithms will be exemplified using the C++ wrapper ([[https://www.khronos.org/opencl/assets/CXX_forOpenCL.html](https://www.khronos.org/opencl/assets/CXX_forOpenCL.html)]([https://www.khronos.org/opencl/assets/CXX_forOpenCL.html](https://www.khronos.org/opencl/assets/CXX_forOpenCL.html)), accessed on May 2024) of OpenCL ([[https://www.khronos.org/opencl/](https://www.khronos.org/opencl/)]([https://www.khronos.org/opencl/](https://www.khronos.org/opencl/)), accessed on May 2024). For those familiar with CUDA, we must emphasize that OpenCL and CUDA are quite similar in essence; both are parallel computing platforms designed to harness the computational power of GPUs for general-purpose computing tasks, providing frameworks for parallel computing on GPUs. They allow developers to write programs that execute in parallel on the GPU, taking advantage of the massive parallelism inherent in GPU architectures. Both OpenCL and CUDA use a similar programming model based on kernels (small functions that are executed in parallel across many threads on the GPU) and provide memory models tailored for GPU architectures (different types of memory, e.g., global, local, constant, with specific access patterns optimized for parallel execution). Both platforms organize computation into threads and thread blocks (CUDA) or work-items and work-groups (OpenCL). These units of parallelism are scheduled and executed on the GPU hardware. The initial tendency when addressing a sieving problem using GPGPU might be to adopt a simple method, where a kernel is launched for each value to be processed. Typically, this is not advisable, because such an approach struggles to handle race conditions effectively: when multiple threads execute simultaneously without proper synchronization, race conditions occur, where the threads attempt to modify the same variable concurrently (in the context of sieving algorithms, if each GPU thread is assigned to mark multiples of a specific prime number, threads may interfere with each other when updating shared data structures like arrays, which can lead to errors or inconsistent results). Such approach also fails to make use of the GPU's local data cache: GPUs are most efficient when they can leverage their fast, local memory (like shared memory among threads in a block) instead of relying on global memory, which is slower to access. A simplistic approach where each thread deals independently with marking composites may not effectively utilize this local memory. Algorithms optimized for GPUs often try to maximize data locality to reduce memory access times and increase throughput. Moreover, launching a separate kernel for each composite to be marked is typically inefficient. It involves considerable overhead in terms of kernel launch and execution management. Efficient GPGPU algorithms usually minimize the number of kernel launches and maximize the work performed per thread, often by allowing each thread to handle multiple data elements or by intelligently grouping related tasks. To optimally exploit the hardware resources we should implement the sieving algorithm at hand with non-trivial kernels and split the sieving process between the host CPU and device GPU, as in Algorithm 1.