Defects and Recommendations for Parallelism
Static code analysis tools are designed to aid software developers to build better quality software in less time, by promoting best practices and detecting defects early in the software development life cycle. A defect can lead to a minor malfunction or cause serious security and safety issues. Thus, identifying, mitigating and resolving defects is an essential part of the software development process.
Parallelware Analyzer and Parallelware Trainer report code defects that constitute errors and issues recommendations to adopt best practices that prevent errors related parallelism.
Defects
📄 PWD001: Invalid OpenMP multithreading datascoping
📄 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
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++
PWR011: Outline loop to increase compiler and tooling code coverage
PWR012: Pass only required fields from derived data types as parameters
PWR013: Avoid copying unused variables to the GPU
PWR014: Out-of-dimension-bounds array access
PWR015: Avoid copying unnecessary array elements to or from the GPU
PWR016: Use separate arrays instead of an Array-of-Structs
PWR018: Call to recursive function within a loop may inhibit vectorization
PWR019: consider interchanging loops to favor vectorization by maximizing inner loop’s trip count
PWR020: consider loop fission to enable vectorization
PWR021: temporary computation can be extracted to a vectorizable loop
PWR022: move invariant conditional out of the loop to facilitate vectorization
PWR024: loop can be rewritten in OpenMP canonical form
PWR025: consider annotating pure function with OpenMP ‘declare simd’
Related blog posts
- Introducing defects and recommendations for parallelism
- Fixing defects in parallel code: an OpenMP example
- Parallelware Analyzer NPB Quickstart
- Using CMake’s compilation database with Parallelware Analyzer
- Interprocedural analysis across source code files with Parallelware Analyzer
- A touch of parallelism: example of NPB CG Benchmark