Packing is an essential loop optimization for handcrafting a high-performance General Matrix Multiplication (GEMM). Packing copies a non-contiguous block of data to a contiguous block to reduce the number of TLB entries required to access it, avoiding expensive TLB misses. When copying data, packing can rearrange elements of the block to decrease the stride between consecutive accesses, improving spatial locality. Until now the use of packing has been limited to handcrafted GEMM implementations and to auto-tuning techniques. Existing loop optimizers, such as Polly and Pluto, either only apply packing to GEMM computations (Polly), or not at all (Pluto). This work proposes GPAT, a generalized packing analysis and code transformation that applies packing, when beneficial, to a generic input loop nest. GPAT is implemented in the Affine dialect of MLIR and evaluated on Polybench/C. GPAT applies packing to benchmarks beyond GEMM and obtains significant speedup compared to current loop optimizers that do not apply packing.

Inline Caching is an important technique used to accelerate operations in dynamically typed language implementations by creating fast paths based on observed program behaviour.Most software stacks that support inline caching use lowlevel, often ad-hoc, Inline-Cache (ICs) data structures for code generation.This work presents CacheIR, a design for inline caching built entirely around an intermediate representation (IR) which: (i) simplifies the development of ICs by raising the abstraction level; and (ii) enables reusing compiled native code through IR matching techniques.Moreover, this work describes WarpBuilder, a novel design for a Just-In-Time (JIT) compiler front-end that directly generates type-specialized code by lowering the CacheIR contained in ICs; and Trial Inlining, an extension to the inline-caching system that allows for context-sensitive inlining of context-sensitive ICs.The combination of CacheIR and WarpBuilder have been powerful performance tools for the SpiderMonkey team, and have been key in providing improved performance with less security risk.

Twitter has withstood the test of time as a successful social networking platform. In many circles globally, the majority of users choose Twitter when choosing a social media outlet for reliable scientific information and news. However, the Twitter application programming interface (API) limitations do not allow for low-cost data science options for academia. It becomes very expensive for academic researchers to gain the full potential of data analytics available from Twitter using a free API account. In this article, we present our big data analytics platform developed at our DaTALab at Lakehead University, Canada, that allows users to focus on their Twitter search criteria and gain access to large amounts of Twitter data at the touch of a button. The platform supports the collection of social media data and applies many filters for cleaning and further use for machine learning (ML) and artificial intelligence (AI)-based systems. Our focus has been primarily on healthcare-related research, which shows the strength of the presented platform. However, the platform itself is malleable to any topic of interest. Data collected and processed are suitable for further AI/ML analysis. We present our platform using a specific healthcare search topic to emphasize the power of our system for future research endeavors in the healthcare field.

Artificial Neural Networks are a widely used computing system implemented for a wide variety of tasks and problems. A common application of such networks is classification problems. However, a significant amount of this research focuses on one and two-dimensional information, such as vectorized data and images. There is limited research performed on three-dimensional media such as video clips. This can be attributed to a lack of adequate resources, available training datasets, hardware constraints, and appropriate frameworks for implementing such networks. This paper attempts to provide an alternate methodology of feeding three-dimensional video data by preprocessing instead of directly inputting to a deep convolutional neural network. By taking sequential segments from multiple frames of a single video clip and combining them into a single image, the temporal dimension of the video can be encoded as a two-dimensional image. This process is called as temporal slicing and repeated for the entire spatial dimension of the video. The end result is spatio-temporal data encoded in a spatial format, which is then propagated through a convolutional neural network as image data. This method is less resource-intensive and is remarkably faster than pre-existing three-dimensional convolutional methods, while achieving significantly higher accuracy compared to the aforementioned network architectures.