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. ...

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 ...

GPUs are known to benefit structured applications with ample parallelism, such as deep learning in a datacenter. Recently, GPUs have shown promise for irregular streaming network tasks. However, the GPU's co-processor dependence on a CPU for task ...

Sparse solvers are heavily used in computational fluid dynamics (CFD), computer-aided design (CAD), and other important application domains. These solvers remain challenging to execute on massively parallel architectures, due to the sequential ...

Bandwidth achieved from local/shared caches and memory is a major performance determinant in Graphics Processing Units (GPUs). These existing sources of bandwidth are often not enough for optimal GPU performance. Therefore, to enhance the performance ...

Centaur is a GPU-centric architecture for building a low-latency approximate k-Nearest-Neighbors network server. We implement a multi-GPU distributed data flow runtime which enables efficient and scalable network request processing on GPUs. The runtime ...

Lock-free algorithms are typically designed and analyzed with adversarial scheduling in mind. However, on real hardware, lock-free algorithms perform much better than the adversarial assumption predicts, suggesting that adversarial scheduling is ...

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 ...

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 ...