Catalog of best practice rules for performance
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Recommendations
PWR001: Declare global variables as function parameters PWR002: Declare scalar variables in the smallest possible scope PWR003: Explicitly declare pure functions PWR004: Declare OpenMP scoping for all variables PWR005: Disable default OpenMP scoping PWR006: Avoid privatization of read-only variables PWR007: Disable implicit declaration of variables PWR008: Declare the intent for each procedure parameter PWR009: Use OpenMP teams to offload work to GPU PWR010: Avoid column-major array access in C/C++ PWR012: Pass only required fields from derived type as parameters PWR013: Avoid copying unused variables to or from the GPU PWR014: Out-of-dimension-bounds matrix access PWR015: Avoid copying unnecessary array elements to or from the GPU PWR016: Use separate arrays instead of an Array-of-Structs PWR017: Using countable while loops instead of for loops may inhibit vectorization PWR018: Call to recursive function within a loop inhibits vectorization PWR019: Consider interchanging loops to favor vectorization by maximizing inner loop’s trip count PWR020: Consider loop fission to enable vectorization PWR021: Consider loop fission with scalar to vector promotion to enable vectorization PWR022: Move invariant conditional out of the loop to avoid redundant computation and potentially enable vectorization PWR023: Add ‘restrict’ for pointer function parameters to hint the compiler that vectorization is safe PWR024: Loop can be rewritten in OpenMP canonical form PWR025: Consider annotating pure function with OpenMP ‘declare simd’ PWR026: Annotate function for OpenMP Offload PWR027: Annotate function for OpenACC Offload PWR028: Remove pointer increment preventing performance optimization PWR029: Remove integer increment preventing performance optimization PWR030: Remove pointer assignment preventing performance optimization for perfectly nested loops PWR031: Replace call to pow by multiplication, division and/or square root PWR032: Avoid calls to mathematical functions with higher precision than required PWR033: Move invariant conditional out of the loop to avoid redundant computations PWR034: Avoid strided array access to improve performance PWR035: Avoid non-consecutive array access to improve performance PWR036: Avoid indirect array access to improve performance PWR037: Potential precision loss in call to mathematical function PWR038: Apply loop sectioning to improve performance PWR039: Consider loop interchange to improve the locality of reference and enable vectorization PWR040: Consider loop tiling to improve the locality of reference PWR042: Consider loop interchange by promoting the scalar reduction variable to an array PWR043: Consider loop interchange by replacing the scalar reduction value PWR044: Avoid unnecessary floating-point data conversions involving constants PWR045: Replace division with a multiplication with a reciprocal PWR046: Replace two divisions with a division and a multiplication PWR048: Replace multiplication/addition combo with an explicit call to fused multiply-add PWR050: Consider applying multithreading parallelism to forall loop PWR051: Consider applying multithreading parallelism to scalar reduction loop PWR052: Consider applying multithreading parallelism to sparse reduction loop PWR053: Consider applying vectorization to forall loop PWR054: Consider applying vectorization to scalar reduction loop PWR055: Consider applying offloading parallelism to forall loop PWR056: Consider applying offloading parallelism to scalar reduction loop PWR057: Consider applying offloading parallelism to sparse reduction loop PWR060: Consider loop fission to separate gather memory access pattern
Defects
PWD002: Unprotected multithreading reduction operation
PWD003: Missing array range in data copy to the GPU
PWD004: Out-of-memory-bounds array access
PWD005: Array range copied to or from the GPU does not cover the used range
PWD006: Missing deep copy of non-contiguous data to the GPU
PWD007: Unprotected multithreading recurrence
PWD008: Unprotected multithreading recurrence due to out-of-dimension-bounds array access
PWD009: Incorrect privatization in parallel region
PWD010: Incorrect sharing in parallel region
PWD011: Missing OpenMP lastprivate clause
Remarks
RMK001: Loop nesting that might benefit from hybrid parallelization using multithreading and SIMD
RMK002: Loop nesting that might benefit from hybrid parallelization using offloading and SIMD
RMK003: Potentially privatizable temporary variable
RMK007: SIMD opportunity within a multi-threaded region
RMK008: SIMD opportunity within an offloaded region
RMK009: Outline loop to increase compiler and tooling code coverage
RMK010: The vectorization cost model states the loop is not a SIMD opportunity due to strided memory accesses in the loop body
RMK012: The vectorization cost model states the loop is not a SIMD opportunity because conditional execution renders vectorization inefficient
RMK013: The vectorization cost model states the loop is not a SIMD opportunity because loops with low trip count unknown at compile time do not benefit from vectorization
RMK014: The vectorization cost model states the loop is not a SIMD opportunity due to unpredictable memory accesses in the loop body
Glossary
Locality of Reference Loop fission Loop interchange Loop sectioning Loop tiling Loop unswitching Loop-carried dependencies Memory access pattern Multithreading Offloading OpenMP Canonical Form OpenMP Parallelization Patterns for performance optimization (page under construction) Perfect-loop nesting Pointer aliasing Row-major and column-major order Scalar to vector promotion Strength reduction Variable scope Variable scoping in the context of OpenMP Vectorization
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