This poster introduces a compiler intermediate language designed for dataflow optimizations within a polyhedral framework. This intermediate representation describes computations at a high level, defines a set of loop and data transformations that can be ...

Programmable accelerators aim to provide the flexibility of traditional CPUs, with greatly improved performance and energy-efficiency. Arguably, the greatest impediment to the widespread adoption of programmable accelerators, like GPUs, is the software ...

Work-efficient task-parallel algorithms enforce ordering between tasks using queuing primitives. Such algorithms offer limited parallelism due to queuing constraints that result in data movement and synchronization bottlenecks. Speculatively relaxing ...

Asymmetric multicore processors (AMP) are necessary for extracting performance in an era of limited power budget and dark silicon. We have efficient symmetric schedulers, efficient asymmetric schedulers for single-threaded workloads, and efficient ...

We propose CogR, a machine-learning based runtime solution, that enables efficient and dynamic resource scheduling and performance optimization for high-level programming interfaces on heterogeneous systems. CogR tightly combines the structural ...

In a post-Moore world, asynchronous task-based parallelism has become a popular paradigm for parallel programming. Auto-parallelizing compilers are also an active area of research, promising improved developer productivity and application performance. ...

Thread packing (TP) is a widely-used technique to improve the efficiency of parallel systems. Despite extensive prior works, relatively little work has been done to investigate its performance inefficiencies. To bridge this gap, we quantify its ...

Irregular computations are commonly seen in many scientific and engineering domains that use unstructured meshes or sparse matrices. The performance of an irregular application is very dependent upon the dataset. This paper poses the following question: "...

Optimizing scientific applications on today's accelerator-based high performance computing systems can be challenging, especially when multiple GPUs and CPUs with heterogeneous memories and persistent non-volatile memories are present. An example is ...

We address a key challenge in programming high-performance applications - achieving portable performance, i.e., the same source code achieves a consistent, high level of performance over the variety of modern parallel processors, including multi-core CPU ...

Computer games are complex performance-critical graphical applications which require specialized GPU hardware. For this reason, GPU drivers often include many heuristics to help optimize throughput. Recently however, new APIs are emerging which sacrifice ...

Modern heterogeneous computing architectures, which couple multi-core CPUs with discrete many-core GPUs (or other specialized hardware accelerators), enable unprecedented peak performance and energy efficiency levels. However, developing applications ...

Commercial hardware transactional memory (TM) systems commonly use coherence messages to detect data conflicts. When a core inside a transaction receives a coherence request for data, it uses this information to determine whether there was a data ...

Modern parallelizing Datalog compilers are employed in industrial applications such as networking and static program analysis. These applications regularly reason about equivalences, e.g., computing bitcoin user groups, fast points-to analyses, and ...

Fast numerical libraries have been a cornerstone of scientific computing for decades, but this comes at a price. Programs may be tied to vendor specific software ecosystems resulting in polluted, non-portable code. As we enter an era of heterogeneous ...

OpenMP is widely used by a number of applications, computational libraries, and runtime systems. As a result, multiple levels of the software stack use OpenMP independently of one another, often leading to nested parallel regions. Although exploiting ...

Distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like D-IrGL and Lux, use a bulk-synchronous parallel (BSP) programming and execution model. BSP permits bulk-communication and uses large messages which are supported ...