Algebraic Data Types (ADTs) are an increasingly common feature in modern programming languages. In many implementations, values of non-nullary, multi-case ADTs are allocated on the heap, which may reduce performance and increase memory usage. This work ...

Repartitioning in a parallel setting can be defined as the task of redistributing data across processes based on a newly imposed grid/layout. Repartitioning is a fundamental problem, with applications in domains that typically involve computation on ...

An important sparse tensor computation is sparse-tensor-dense-matrix multiplication (SpTM), which is used in tensor decomposition and applications. SpTM is a multi-dimensional analog to sparse-matrix-dense-matrix multiplication (SpMM). In this article, we ...

While vector architecture is widely employed in processors for neural networks, signal processing, and high-performance computing; however, its performance is limited by inefficient column-major memory access. The column-major access limitation originates ...

Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness established practices ...

Racetrack memory (RTM), which consists of multiple domain block clusters (DBC) and access ports, is a novel non-volatile memory and has potential as scratchpad memory (SPM) in embedded devices due to its high density and low access latency. However, too ...

While data locality and cache performance have been investigated in great depth by prior research (in the context of both high-end systems and embedded/mobile systems), one of the important characteristics of prior approaches is that they transform loop ...

Log-structured merge (LSM) trees have emerged as one of the most commonly used disk-based data structures in modern data systems. LSM-trees employ out-of-place ingestion to support high throughput for writes, while their immutable file structure allows ...

Log-structured merge (LSM) trees are widely used as the storage layer of modern NoSQL data stores, as they offer efficient ingestion performance. To enable competitive read performance and reduce space amplification, LSM-trees re-organize data layout on ...

The widespread popularity of deep neural networks (DNNs) has made it an important workload in modern datacenters. Training DNNs is both computation-intensive and memory-intensive. While prior works focus on training parallelization (e.g., data ...

We present optimizations to improve communication performance by reducing on-node data movement for a class of distributed memory applications. The primary concept is to eliminate the data movement associated with packing and unpacking subsets of the ...

Corporations today collect data at an unprecedented and accelerating scale, making the need to run queries on large datasets increasingly important. Technologies such as columnar block-based data organization and compression have become standard practice ...

This demonstration showcases Chestnut, a data layout generator for in-memory object-oriented database applications. Given an application and a memory budget, Chestnut generates a customized in-memory data layout and the corresponding query plans that ...

In cloud environments, data-intensive applications have been widely deployed to solve non-trivial applications, while cloud-based storage systems usually fail to provide desirable performance and efficiency when running those data-intensive applications. ...

With the development of convolutional neural networks (CNNs), their high computational complexity and energy consumption become significant problems. Many CNN inference accelerators are proposed to reduce the consumption. Most of them are based on 32-bit ...

In modern computing hardware, the performance gap between processor and memory is one of the most significant factors that limits overall performance improvement of computing system. Also, with the advent of multicore and manycore system, memory ...

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently, general-purpose graphics processing units GPUs have become available as high-performance computing resources at large scale. We report on ...

Layout and placement of shared data structures is critical to achieving scalable performance on heterogeneous memory architectures. While recent research has established the importance of data organization and developed mechanisms for data layout ...

Data-structures can benefit from dynamic data layout modifications when the size or the shape of the data structure changes during the execution, or when different phases in the program execute different workloads. However, in a modern multi-core ...

Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve state-of-the-art recognition accuracy. Due to the substantial compute and memory operations, however, they require significant execution time. The massive parallel computing ...