Sparse computations, such as sparse matrix-dense vector multiplication, are notoriously hard to optimize due to their irregularity and memory-boundedness. Solutions to improve the performance of sparse computations have been proposed, ranging from hardware-based such as gather-scatter instructions, to software ones such as generalized and dedicated sparse formats, used together with specialized executor programs for different hardware targets. These sparse computations are often performed on read-only sparse structures: while the data themselves are variable, the sparsity structure itself does not change. Indeed, sparse formats such as CSR have a typically high cost to insert/remove nonzero elements in the representation. The typical use case is to not modify the sparsity during possibly repeated computations on the same sparse structure.

Benchmarking to characterize specific software or hardware features is an error-prone, arduous and repetitive task. Designing a specialized experimental setup frequently requires writing new scripts or ad-hoc programs in order to properly exhibit interesting performance effects, using code changes and hardware events measurements. These artifacts may have limited reusability for subsequent experiments, since they are dependent on specific problems and, in some cases, platforms. To improve productivity and reproducibility of such experiments, which are often investigative in nature, we introduce MARTA: a fully customizable toolkit that aims to increase productivity by generating benchmark templates, compiling them, and profiling the regions of interest (RoI) specified using hardware events, and performing static code analysis. MARTA can also be applied on existing code regions of interest, it only requires to write a simple configuration file. In an orthogonal dimension, the system is able to run various statistical analyses on the measurements collected. MARTA uses data mining and machine learning or AI-based techniques for classification and regression, automatically extracting the features of the experimental setup which have the most impact on performance or whichever other metric of interest, given a large set of experiments and dimensions to consider. These post-processing tasks are valuable for deriving knowledge from experiments and are not included in most profiling tools. We also provide a set of cases of study to illustrate the ability of MARTA to conveniently create a reliable and reproducible setup for high-performance computing experiments, investigating three vastly different performance effects on modern processors.

Recent manycore processors are kept coherent using scalable distributed directories. A paramount example is the Xeon Phi Knights Landing. It features 38 tiles packed in a single die, organized into a 2D mesh. Before accessing remote data, tiles need to query the distributed directory. The effect of this coherence traffic is poorly understood. We show that the apparent UMA behavior results from the degradation of the peak performance. We develop ways to optimize the coherence traffic, the core-to-core-affinity, and the scheduling of a set of tasks on the mesh, leveraging the unique characteristics of processor units stemming from process variations.