OpenCL on Intel® CPUs
Intel® supports OpenCL on CPUs, which allows you to tap into the parallel computing capabilities of multi-core processors. Intel CPUs can execute OpenCL kernels on multiple CPU threads, taking advantage of Intel advanced vector processing units such as Intel® AVX2 and Intel® AVX-512.
Key Features:
- Thread-level parallelism: OpenCL workloads can scale efficiently across many CPU cores.
- SIMD vectorization: Utilizing Intel® advanced vector extensions (AVX), OpenCL kernels can process multiple data points per clock cycle.
- Memory optimizations: Intel® implementation includes optimizations for cache coherence and memory bandwidth, ensuring efficient data access patterns.
OpenCL on Intel® GPUs
The integrated GPUs found on Intel® Core™ processors are designed for power efficiency and parallel data processing, making them well-suited for OpenCL applications requiring graphics processing, AI workloads, and media processing.
Key Features:
-
Fine-grained parallelism: Intel® GPUs are optimized for fine-grained parallel tasks. OpenCL kernels can efficiently run many small threads, with each GPU execution unit handling multiple threads simultaneously.
-
Unified memory architecture: Intel® GPU and CPU share memory, reducing the overhead associated with transferring data between devices and enabling seamless execution of OpenCL kernels across both devices.
Intel® oneAPI compatibility
Intel® oneAPI provides a unified programming model for its diverse hardware platforms. OpenCL remains an integral part of this framework. Intel® oneAPI Data Parallel C++ (DPC++) Compiler allows applications to use the latest SYCL features on OpenCL devices.
While oneAPI encourages developers to transition to SYCL, Intel® ensures backward compatibility with OpenCL and supports interoperability between them, enabling applications to move incrementally.
Debug and Profile
Intel® oneAPI DPC++/C++ Compiler
Purpose: Write portable, high-performance code for CPUs and GPUs using DPC++ and OpenCL.
Features:
- Calls OpenCL kernels directly from SYCL applications.
- Allows to write parallel code that runs on heterogeneous hardware with OpenCL support for fine-grained control.
- Applies various optimizations (such as vectorization, loop unrolling, and memory optimizations) to improve the performance of OpenCL kernels.
Usage:
- Integrates OpenCL kernels into DPC++ applications to leverage existing OpenCL code while benefiting from the high-level parallelism of DPC++.
- Utilizes Intel® oneAPI libraries and tools to maximize the performance of OpenCL code when running on Intel® architectures.
Intel® VTune Profiler
Goal: Profile the performance of OpenCL code running on CPUs and GPUs.
Features:
- Provides a detailed analysis of hotspots in OpenCL kernels.
- Provides CPU and GPU utilization and power efficiency insights
- Conducts Memory bandwidth analysis.
- Analyzes thread concurrency and CPU core usage.
Usage:
- Profiles OpenCL kernels alongside other CPU tasks.
- Allows you to identify where the performance bottlenecks occur while executing OpenCL code.
- Supports analysis down to the source line of your OpenCL kernels.
Intercept Layer for OpenCL Applications
Purpose: Enable better visibility into API calls, resource usage, and kernel execution to facilitate performance improvements.
Features:
- Captures and logs all OpenCL API calls for detailed analysis and debugging.
- Provides real-time performance metrics, including kernel execution times and memory usage.
- Offers support for error checking and validation of OpenCL calls.
-
Supports profiling of resource management and memory allocation patterns in OpenCL applications.
Usage: Monitors application performance and troubleshoot issues related to resource management, kernel execution, and memory optimization.
Intel® Distribution for GDB
Purpose: Debug CPU and GPU code in OpenCL applications with source-level debugging for both host and device code.
Key Features
- Allows inspection of variable states and memory interactions.
- Analyzes control flow and variable values during execution.
- Identifies memory allocation errors or initialization issues.
- Tracks execution across multiple devices simultaneously to catch cross-device bugs.
Usage: Identifies and resolves host and device code issues across Intel heterogeneous architectures.
Intel® Advisor
Goal: Provide performance profiling and analysis for OpenCL™ kernels running on Intel® GPUs and CPUs.
Features:
- Conducts GPU hotspot analysis.
- Measures GPU kernel execution time and analyzes GPU utilization.
- Tracks memory bandwidth, occupancy, and synchronization between CPU and GPU.
-
Provides detailed insights into the performance of OpenCL kernels.
Usage:
- Identifies where your OpenCL kernels spend the most time.
- Tracks metrics such as GPU memory transfer rates and GPU core utilization.
- Optimizes the CPU-GPU interaction and minimizes data transfer overhead.
Notices and Disclaimers
Intel technologies may require enabled hardware, software or service activation.
No product or component can be absolutely secure.
Your costs and results may vary.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document.
The products described may contain design defects or errors known as errata which ay cause the product to deviate from published specifications. Current characterized errata are available on request.
Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade.