Since the 1990s, many implementations of polyhedral compilers have been written and distributed, either as source-to-source translating compilers or integrated into wider-purpose compilers. This article provides a survey on those various available implementations as of today, 2024. First, we list and describe most commonly available polyhedral schedulers and compiler implementations. Then, we compare the general-purpose polyhedral compilers using two main criteria—robustness and performance—on the PolyBench/C set of benchmarks.

The study of numerical models for the human body has become a major focus of the research community in biology and medicine. For instance, numerical ionic models of a complex organ, such as the heart, must be able to represent individual cells and their interconnections through ionic channels, forming a system with billions of cells, and requiring efficient code to handle such a large system. The modeling of the electrical system of the heart combines a compute-intensive kernel that calculates the intensity of current flowing through cell membranes, and feeds a linear solver for computing the electrical potential of each cell.

Testing concurrent systems remains an uncomfortable problem for developers. The common industrial practice is to stress-test a system against large workloads, with the hope of triggering enough corner-case interleavings that reveal bugs. However, stress testing is often inefficient and its ability to get coverage of interleavings is unclear. In reaction, the research community has proposed the idea of systematic testing, where a tool takes over the scheduling of concurrent actions so that it can perform an algorithmic search over the space of interleavings.We present an experience paper on the application of systematic testing to several case studies. We separate the algorithmic advancements in prior work (on searching the large space of interleavings) from the engineering of their tools. The latter was unsatisfactory; often the tools were limited to a small domain, hard to maintain, and hard to extend to other domains. We designed Nekara, an open-source cross-platform library for easily building custom systematic testing solutions.We show that (1) Nekara can effectively encapsulate state-of-the-art exploration algorithms by evaluating on prior bench-marks, and (2) Nekara can be applied to a wide variety of scenarios, including existing open-source systems as well as production distributed services of Microsoft Azure. Nekara was easy to use, improved testing, and found multiple new bugs.

X10 is a partitioned global address space programming language that supports the notion of places ; a place consists of some data and some lightweight tasks called activities. Each activity runs at a place and may invoke a place-change operation (using the at-construct) to synchronously perform some computation at another place. These place-change operations can be very expensive, as they need to copy all the required data from the current place to the remote place. However, identifying the necessary number of place-change operations and the required data during each place-change operation are non-trivial tasks, especially in the context of irregular applications (like graph applications) that contain complex code with large amounts of cross-referencing objects—not all of those objects may be actually required, at the remote place. In this article, we present AT-Com, a scheme to optimize X10 code with place-change operations. AT-Com consists of two inter-related new optimizations: (i) AT-Opt, which minimizes the amount of data serialized and communicated during place-change operations, and (ii) AT-Pruning, which identifies/elides redundant place-change operations and does parallel execution of place-change operations. AT-Opt uses a novel abstraction, called abstract-place-tree , to capture place-change operations in the program. For each place-change operation, AT-Opt uses a novel inter-procedural analysis to precisely identify the data required at the remote place in terms of the variables in the current scope. AT-Opt then emits the appropriate code to copy the identified data-items to the remote place. AT-Pruning introduces a set of program transformation techniques to emit optimized code such that it avoids the redundant place-change operations. We have implemented AT-Com in the x10v2.6.0 compiler and tested it over the IMSuite benchmark kernels. Compared to the current X10 compiler, the AT-Com optimized code achieved a geometric mean speedup of 18.72× and 17.83× on a four-node (32 cores per node) Intel and two-node (16 cores per node) AMD system, respectively.

X10 is a partitioned global address space (PGAS) programming language that supports the notion of places; a place consists of some data and some lightweight tasks called activities. Each activity runs at a place and may invoke a place-change operation (using the at-construct) to synchronously perform some computation at another place. These place-change operations need to copy all the required data from the current place to the remote place. However, identifying the required data during each place-change operation is a non-trivial task, especially in the context of irregular applications (like graph applications) that contain large amounts of cross-referencing objects - not all of those objects may be actually required, at the remote place. In this paper, we present a new optimization AT-Opt that minimizes the amount of data serialized and communicated during place-change operations.