Consequently, data movement and storage is expected to consume more than 70% of the total system power. It is expected that by 2018, node concurrency in an exascale system will increase by hundreds of times, whereas, memory bandwidth will expand by only 10 to 20 times.
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Despite the capped growth of the peak CPU speed, the aggregate speed of a CMP keeps increasing as more cores get into a single chip. The second hardware more » trend is the growing gap between memory bandwidth and the aggregate speed-that is, the sum of all cores' computing power-of a Chip Multiprocessor (CMP). Some evidences are already shown on Graphic Processing Units (GPU): Irregular data accesses (e.g., indirect references A]) and conditional branches are limiting many GPU applications' performance at a level an order of magnitude lower than the peak of GPU. With more processors produced through a massive integration of simple cores, future systems will increasingly favor regular data-level parallel computations, but deviate from the needs of applications with complex patterns. The first is the increasing sensitivity of processors' throughput to irregularities in computation. The development of modern processors exhibits two trends that complicate the optimizations of modern software. We have evaluated Juggler on an NVIDIA Tesla P100 GPU and obtained up to 31% performance improvement against global barrier based implementation, with minimal runtime overhead. Juggler requires no or little modification to the source code, and once launched, the runtime entirely runs on the GPU without relying on the host through the entire execution. The Juggler framework takes applications embedding OpenMP 4.5 tasks as input and executes them on the GPU via an efficient in-device runtime, hence eliminating the need for kernel-wide global synchronization. Such a scheme will enable faster execution by utilizing all internal computation elements inside the GPU and eliminating unnecessary waits during device-wide global barriers.In this paper, we propose Juggler, more » a task-based execution scheme for GPU workloads with data dependences.
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To efficiently run applications with interblock data dependences, we need fine-granular task-based execution models that will treat SMs inside a GPU as stand-alone parallel processing units. However, the existence of data dependences across thread blocks may significantly impact the speedup by requiring global synchronization across multiprocessors (SMs) inside the GPU.
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Scientific applications with single instruction, multiple data (SIMD) computations show considerable performance improvements when run on today's graphics processing units (GPUs).