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Introduction

The oneAPI samples repository provides code samples for Intel oneAPI toolkits.

We recommend checking out a specific stable release version of the repository. The version of the repository you fetch should match the version of the oneAPI compiler you are using. View available stable releases. The latest versions (2021.4.0) of code samples on the master branch are not guaranteed to be stable.

Sample Details

The oneAPI sample repository is organized as follows:

Known Issues or Limitations

On Windows Platform

  • If you are using Visual Studio 2019, Visual Studio 2019 version 16.4.0 or newer is required.
  • Windows support for the FPGA code samples is limited to the FPGA emulator and optimization reports. Compile targets for FPGA hardware are provided on Linux only. See any FPGA code sample for more details.
  • If you encounter a compilation error when building a sample program, such as the example error below, the directory path of the sample may be too long. The workaround is to move the sample to a temp directory.
    • Example error: *Error MSB6003 The specified task executable dpcpp.exe could not be run .......

Additional Resources

Contributing

See CONTRIBUTING wiki for more information.

New Code Samples

Version Introduced Sample Name Description
2021.4.0 Pub: Data Parallel C++:
Chapter 01 - Introduction
Collection of Code samples for the chapter
- Fig_1_1_hello.cpp - Hello data-parallel programming
- Fig_1_3_race.cpp - Adding a race condition to illustrate a point about being asynchronous
- Fig_1_4_lambda.cpp - Lambda function in C++ code
- Fig_1_6_functor.cpp - Function object instead of a lambda (more on this in Chapter 10)
2021.4.0 Pub: Data Parallel C++:
Chapter 02 - Where Code Executes
Collection of Code samples for the chapter
- Fig_2_2_simple_program.cpp - Simple SYCL program
- Fig_2_7_implicit_default_selector.cpp - Implicit default device selector through trivial construction of a queue
- Fig_2_9_host_selector.cpp - Selecting the host device using the host_selector class
- Fig_2_10_cpu_selector.cpp - CPU device selector example
- Fig_2_12_multiple_selectors.cpp - Example device identification output from various classes of device selectors and demonstration that device selectors can be used for cons
- Fig_2_13_gpu_plus_fpga.cpp - Creating queues to both GPU and FPGA devices
- Fig_2_15_custom_selector.cpp - Custom selector for Intel Arria FPGA device
- Fig_2_18_simple_device_code.cpp - Submission of device code
- Fig_2_22_simple_device_code_2.cpp - Submission of device code
- Fig_2_23_fallback.cpp - Fallback queue example
2021.4.0 Pub: Data Parallel C++:
Chapter 03 - Data Management
Collection of Code samples for the chapter
- Fig_3_4_usm_explicit_data_movement.cpp - USM explicit data movement
- Fig_3_5_usm_implicit_data_movement.cpp - USM implicit data movement
- Fig_3_6_buffers_and_accessors.cpp - Buffers and accessors
- Fig_3_10_in_order.cpp - In-order queue usage
- Fig_3_11_depends_on.cpp - Using events and depends_on
- Fig_3_13_read_after_write.cpp - Read-after-Write
- Fig_3_15_write_after_read_and_write_after_write.cpp - Write-after-Read and Write-after-Write
2021.4.0 Pub: Data Parallel C++:
Chapter 04 - Expresssing Parallelism
Collection of Code samples for the chapter
- Fig_4_5_vector_add.cpp - Expressing a vector addition kernel with parallel_for
- Fig_4_6_matrix_add.cpp - Expressing a matrix addition kernel with parallel_for
- Fig_4_7_basic_matrix_multiply.cpp - Expressing a naïve matrix multiplication kernel for square matrices, with parallel_for
- Fig_4_13_nd_range_matrix_multiply.cpp - Expressing a naïve matrix multiplication kernel with ND-range parallel_for
- Fig_4_20_hierarchical_matrix_multiply.cpp - Expressing a naïve matrix multiplication kernel with hierarchical parallelism
- Fig_4_22_hierarchical_logical_matrix_multiply.cpp - Expressing a naïve matrix multiplication kernel with hierarchical parallelism and a logical range
2021.4.0 Pub: Data Parallel C++:
Chapter 05 - Error Handling
Collection of Code samples for the chapter
- Fig_5_1_async_task_graph.cpp - Separation of host program and task graph executions
- Fig_5_2_sync_error.cpp - Creating a synchronous error
- Fig_5_3_async_error.cpp - Creating an asynchronous error
- Fig_5_4_unhandled_exception.cpp - Unhandled exception in C++
- Fig_5_5_terminate.cpp - std::terminate is called when a SYCL asynchronous exception isn’t handled
- Fig_5_6_catch_snip.cpp - Pattern to catch sycl::exception specifically
- Fig_5_7_catch.cpp - Pattern to catch exceptions from a block of code
- Fig_5_8_lambda_handler.cpp - Example asynchronous handler implementation defined as a lambda
- Fig_5_9_default_handler_proxy.cpp - Example of how the default asynchronous handler behaves
2021.4.0 Pub: Data Parallel C++:
Chapter 06 - Unified Shared Memory
Collection of Code samples for the chapter
- Fig_6_5_allocation_styles.cpp - Three styles for allocation
- Fig_6_6_usm_explicit_data_movement.cpp - USM explicit data movement example
- Fig_6_7_usm_implicit_data_movement.cpp - USM implicit data movement example
- Fig_6_8_prefetch_memadvise.cpp - Fine-grained control via prefetch and mem_advise
- Fig_6_9_queries.cpp - Queries on USM pointers and devices
2021.4.0 Pub: Data Parallel C++:
Chapter 07 - Buffers
Collection of Code samples for the chapter
- Fig_7_2_3_4_creating_buffers.cpp - Creating buffers, Part 1 - Figure 7-3. Creating buffers, Part 2 - Figure 7-4. Creating buffers, Part 3
- Fig_7_5_buffer_properties.cpp - Buffer properties
- Fig_7_8_accessors_simple.cpp - Simple accessor creation
- Fig_7_10_accessors.cpp - Accessor creation with specified usage
2021.4.0 Pub: Data Parallel C++:
Chapter 08 - Scheduling Kernals and Data Movement
Collection of Code samples for the chapter
- Fig_8_3_linear_dependence_in_order.cpp - Linear dependence chain with in-order queues
- Fig_8_4_linear_dependence_events.cpp - Linear dependence chain with events
- Fig_8_5_linear_dependence_buffers.cpp - Linear dependence chain with buffers and accessors
- Fig_8_6_y_in_order.cpp - Y pattern with in-order queues
- Fig_8_7_y_events.cpp - Y pattern with events
- Fig_8_8_y_buffers.cpp - Y pattern with accessors
2021.4.0 Pub: Data Parallel C++:
Chapter 09 - Communication and Synchronization
Collection of Code samples for the chapter
- Fig_9_4_naive_matrix_multiplication.cpp - The naïve matrix multiplication kernel from Chapter 4
- Fig_9_7_local_accessors.cpp - Declaring and using local accessors
- Fig_9_8_ndrange_tiled_matrix_multiplication.cpp - Expressing a tiled matrix multiplication kernel with an ND-range parallel_for and work-group local memory
- Fig_9_9_local_hierarchical.cpp - Hierarchical kernel with a local memory variable
- Fig_9_10_hierarchical_tiled_matrix_multiplication.cpp - A tiled matrix multiplication kernel implemented as a hierarchical kernel
- Fig_9_11_sub_group_barrier.cpp - Querying and using the sub_group class
- Fig_9_13_matrix_multiplication_broadcast.cpp - Matrix multiplication kernel includes a broadcast operation
- Fig_9_14_ndrange_sub_group_matrix_multiplication.cpp - Tiled matrix multiplication kernel expressed with ND-range parallel_for and sub-group collective functions
2021.4.0 Pub: Data Parallel C++:
Chapter 10 - Defining Kernels
Collection of Code samples for the chapter
- Fig_10_2_kernel_lambda.cpp - Kernel defined using a lambda expression
- Fig_10_3_optional_kernel_lambda_elements.cpp - More elements of a kernel lambda expression, including optional elements
- Fig_10_4_named_kernel_lambda.cpp - Naming kernel lambda expressions
- Fig_10_5_unnamed_kernel_lambda.cpp - Using unnamed kernel lambda expressions
- Fig_10_6_kernel_functor.cpp - Kernel as a named function object
- Fig_10_7_opencl_source_interop.cpp - Kernel created from OpenCL C kernel source
- Fig_10_8_opencl_object_interop.cpp - Kernel created from an OpenCL kernel object
- Fig_10_9_kernel_lambda_build_options.cpp - Compiling kernel lambdas with build options
2021.4.0 Pub: Data Parallel C++:
Chapter 11 - Vectors
Collection of Code samples for the chapter
- Fig_11_6_load_store.cpp - Use of load and store member functions.
- Fig_11_7_swizzle_vec.cpp - Example of using the swizzled_vec class
- Fig_11_8_vector_exec.cpp - Vector execution example
2021.4.0 Pub: Data Parallel C++:
Chapter 12 - Device Information
Collection of Code samples for the chapter
- Fig_12_1_assigned_device.cpp - Device we have been assigned by default
- Fig_12_2_try_catch.cpp - Using try-catch to select a GPU device if possible, host device if not
- Fig_12_3_device_selector.cpp - Custom device selector—our preferred solution
- Fig_12_4_curious.cpp - Simple use of device query mechanisms: curious.cpp
- Fig_12_6_very_curious.cpp - More detailed use of device query mechanisms: verycurious.cpp
- Fig_12_7_invocation_parameters.cpp - Fetching parameters that can be used to shape a kernel
2021.4.0 Pub: Data Parallel C++:
Chapter 13 - Practical Tips
Collection of Code samples for the chapter
- Fig_13_4_stream.cpp - sycl::stream
- Fig_13_6_common_buffer_pattern.cpp - Common pattern—buffer creation from a host allocation
- Fig_13_7_common_pattern_bug.cpp - Common bug: Reading data directly from host allocation during buffer lifetime
- Fig_13_8_host_accessor.cpp - Recommendation: Use a host accessor to read kernel result
- Fig_13_9_host_accessor_for_init.cpp - Recommendation: Use host accessors for buffer initialization and reading of results
- Fig_13_10_host_accessor_deadlock.cpp - Bug (hang!) from improper use of host_accessors
2021.4.0 Pub: Data Parallel C++:
Chapter 14 - Common Parallel Patterns
Collection of Code samples for the chapter
- Fig_14_8_one_reduction.cpp - Reduction expressed as an ND-range data-parallel kernel using the reduction library
- Fig_14_11_user_defined_reduction.cpp - Using a user-defined reduction to find the location of the minimum value with an ND-range kernel
- Fig_14_13_map.cpp - Implementing the map pattern in a data-parallel kernel
- Fig_14_14_stencil.cpp - Implementing the stencil pattern in a data-parallel kernel
- Fig_14_15_local_stencil.cpp - Implementing the stencil pattern in an ND-range kernel, using work-group local memory
- Fig_14_18-20_inclusive_scan.cpp - Implementing a naïve reduction expressed as a data-parallel kernel
- Fig_14_22_local_pack.cpp - Using a sub-group pack operation to build a list of elements needing additional postprocessing
- Fig_14_24_local_unpack.cpp - Using a sub-group unpack operation to improve load balancing for kernels with divergent control flow
2021.4.0 Pub: Data Parallel C++:
Chapter 15 - Programming for GPUs
Collection of Code samples for the chapter
- Fig_15_3_single_task_matrix_multiplication.cpp - A single task matrix multiplication looks a lot like CPU host code
- Fig_15_5_somewhat_parallel_matrix_multiplication.cpp - Somewhat-parallel matrix multiplication
- Fig_15_7_more_parallel_matrix_multiplication.cpp - Even more parallel matrix multiplication
- Fig_15_10_divergent_control_flow.cpp - Kernel with divergent control flow
- Fig_15_12_small_work_group_matrix_multiplication.cpp - Inefficient single-item, somewhat-parallel matrix multiplication
- Fig_15_18_columns_matrix_multiplication.cpp - Computing columns of the result matrix in parallel, not rows
2021.4.0 Pub: Data Parallel C++:
Chapter 16 - Programming for CPUs
Collection of Code samples for the chapter
- Fig_16_6_stream_triad.cpp - DPC++ STREAM Triad parallel_for kernel code
- Fig_16_12_forward_dep.cpp - Using a sub-group to vectorize a loop with a forward dependence
- Fig_16_18_vector_swizzle.cpp - Using vector types and swizzle operations in the single_task kernel
2021.4.0 Pub: Data Parallel C++:
Chapter 17 - Programming for FPGA
Collection of Code samples for the chapter
- Fig_17_9_fpga_selector.cpp - Choosing an FPGA device at runtime using the
- Fig_17_11_fpga_emulator_selector.cpp - Using fpga_emulator_selector for rapid development and debugging
- Fig_17_17_ndrange_func.cpp - Multiple work-item (16 × 16 × 16) invocation of a random number generator
- Fig_17_18_loop_func.cpp - Loop-carried data dependence (state)
- Fig_17_20_loop_carried_deps.cpp - Loop with two loop-carried dependences (i.e., i and a)
- Fig_17_22_loop_carried_state.cpp - Random number generator that depends on previous value generated
- Fig_17_31_inter_kernel_pipe.cpp - Pipe between two kernels: (1) ND-range and (2) single task with a loop
2021.4.0 Pub: Data Parallel C++:
Chapter 18 - Libraries
Collection of Code samples for the chapter
- Fig_18_1_builtin.cpp - Using std::log and sycl::log
- Fig_18_7_swap.cpp - Using std::swap in device code
- Fig_18_11_std_fill.cpp - Using std::fill
- Fig_18_13_binary_search.cpp - Using binary_search
- Fig_18_15_pstl_usm.cpp - Using Parallel STL with a USM allocator Errata - code samples for 18-10, 18-12, 18-14, and 19-17 are not in the repository
2021.4.0 Pub: Data Parallel C++:
Chapter 19 - Memory Model and Atomics
Collection of Code samples for the chapter
- Fig_19_3_data_race.cpp - Kernel containing a data race
- Fig_19_6_avoid_data_race_with_barrier.cpp - Avoiding a data race using a barrier
- Fig_19_7_avoid_data_race_with_atomics.cpp - Avoiding a data race using atomic operations
- Fig_19_15_buffer_and_atomic_ref.cpp - Accessing a buffer via an explicitly created atomic_ref
- Fig_19_16_atomic_accessor.cpp - Accessing a buffer via an atomic_ref implicitly created by an atomic accessor
- Fig_19_18_histogram.cpp - Computing a histogram using atomic references in different memory spaces
- Fig_19_19-20_device_latch.cpp - Combining Figure 19-20. Using and building a simple device-wide latch on top of atomic references Errata - code samples for 18-10, 18-12, 18-14, and 19-17 are not in the repository
2021.4.0 Pub: Data Parallel C++:
Chapter 20 - Epilogue Future Direction
Collection of Code samples for the chapterEpilogue source code examples: Future Direction of DPC++
- Fig_ep_1_mdspan.cpp - Attaching accessor-like indexing to a USM pointer using mdspan
- Fig_ep_2-4_generic_space.cpp - Storing pointers to a specific address space in a class - Figure EP-3. Storing pointers to the generic address space in a class - Figure EP-4. Storing pointers with an optional address space in a class
- Fig_ep_5_extension_mechanism.cpp - Checking for Intel sub-group extension compiler support with #ifdef
- Fig_ep_6_device_constexpr.cpp - Specializing kernel code based on device aspects at kernel compile time
- Fig_ep_7_hierarchical_reduction.cpp - Using hierarchical parallelism for a hierarchical reduction
2021.4.0 Intel® Python Scikit-learn Extension Getting Started This sample illustrates how to do Image classification using SVM classifier from Python API package SKlearnex with the use of Intel® oneAPI Data Analytics Library (oneDAL).
2021.4.0 Merge Sort A Reference design demonstrating merge sort on an Intel® FPGA
2021.4.0 Private Copies An Intel® FPGA tutorial demonstrating how to use the private_copies attribute to trade off the resource use and the throughput of a DPC++ FPGA program
2021.4.0 Stall Enable An Intel® FPGA tutorial demonstrating the use_stall_enable_clusters attribute

Total Samples: 158

License

Code samples are licensed under the MIT license. See License.txt for details.

Third-party program Licenses can be found here: third-party-programs.txt

Report Generated on: September 09, 2021