International Business Machines Corporation

Bardia Mahjour

Wai Hung Tsang

Ettore Tiotto

Jintao Zhang

Prasanth Chatarasi

Maryam Dehnavi

Jun Shirako

Prithayan Barua

Alberto Mannari

Avery Laird

Bangtian Liu

A vectorization factor to be used in vectorization of an epilogue loop in program code is automatically selected. The automatically selecting includes selecting the vectorization factor from a plurality of candidate vectorization factors based on one or more considerations relating to vectorizing the epilogue loop. The vectorization factor that is automatically selected is used in vectorizing the epilogue loop.

Selecting an epilogue vectorization factor for use in computer processing

A computer implemented method for merging loops. A number of processor units identifies loops in computer code. The loops are sequences of instructions that are repeated until conditions for the loops are reached. The number of processor units creates a tree comprising nodes that represent the loops and edges that represent relationships between nodes. The number of processor units utilizes the tree to identify a pair of candidate loops from sibling nodes. The number of processor units creates a new loop from the pair of candidate loops with an expanded iteration space based on iteration spaces for the pair of candidate loops in response to the pair of candidate loops being eligible for merging.

Loop index set merging optimization for program instructions

Vectorization of program code by way of a method that includes obtaining program code to be compiled. The program code has a loop that includes a control-flow having divergent branch conditions. The method includes compiling the program code to produce compiled code. The compiling includes performing compile-time analysis of the loop based on an assumption of dynamic uniformity, and determining from the analysis a vectorizable access pattern of the loop. The compiling also includes vectorizing the loop as part of the compiling the program code, including compiling the loop. The vectorizing includes providing run-time checks in the compiled code. The run-time checks are configured for checking for dynamically uniform conditions for vector processing at run-time to control program execution flow based on a result of the checking. The method also includes outputting the compiled code for execution.

Control flow auto-vectorization using run-time checks and compile-time analysis

A loop within computer code is transformed to minimize loop iterations. A determination is made using statistical information relating to the loop whether the loop that has an early exit indication is to be transformed to minimize iterations of the loop. Based on determining that the loop is to be transformed, the loop is transformed.

Transformation of a loop within computer code to minimize iterations

Aspects include modeling data cache utilization for each loop in a loop nest; estimating total data cache lines fetched in one iteration of the loop; and determining the possibility of data cache reuse across loop iterations using data cache lines fetched and associativity constraints. Aspects also include estimating, for memory reference pairs, reuse by one reference of data cache line fetched by another; estimating total number of cache misses for all iterations of the loop; and estimating total number of cache misses of a reference for iterations of a next outer loop as equal to total cache misses for an entire inner loop. Aspects further include estimating memory cost of a loop unroll and jam transformation, without performing the transformation; and extending a data cache model to estimate best unroll-and-jam factors for the loop nest, capable of minimizing total cache misses incurred by the memory references in the loop body.

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Bardia Mahjour