Achieving Exascale Capabilities through Heterogeneous Computing

Abstract

have demanded increasingly powerful computer systems. High-performance computing (HPC) has steadily pushed supercomputers to greater computational capabilities, with petascale computing (10 15 floating-point operations per second [flops]) first being achieved in 2008. 1 The current fastest supercomputer is capable of 33.86 petaflops (see www.top500 .org). These HPC systems perform massive computations to drive important scientific experiments leading to impactful discoveries, from designing more efficient fuels and engines to engineering safer bridges and buildings, from modeling global climate phenomena to exploring the origins of the universe.The next HPC milestone is that of developing an exascale supercomputer that can achieve more than an exaflop (10 18 or one billion, billion flops) on critical scientific computing applications. Such exascale supercomputers are envisioned to comprise at least 100,000 interconnected servers or nodes, implying that each node has an individual computing capability of greater than 10 teraflops (Tflops) on real applications. Note that a modern high-end discrete GPU today achieves a peak of only about three doubleprecision teraflops.The challenges associated with exascale computing, however, extend far beyond merely achieving a certain number of floatingpoint calculations per second. To feed such high levels of computation, both the system's memory and internode communication bandwidths must increase dramatically beyond current levels. The energy efficiency of the supercomputer must improve by orders of magnitude to enable the operation within practical datacenter power-delivery capabilities of a few tens of megawatts. With more than 100,000 nodes, significant advances in resilience and reliability are also required to keep the overall machine up and running. In this article, we describe AMD Research's vision for exascale computing, and in particular, how we see heterogeneous computing as the path forward. AMD's vision for exascale computingAn exascale system consists of the hardware implementing the computational resources, as well as the software needed to efficiently write, tune, and execute applications on the hardware.

Towards ultra-scale Branch-and-Bound using a high-productivity language

“…Another observation that can be made from the last editions of Top500 is that the cores are mostly supplied in low-energy computing resources (GPU, MIC, etc.). Therefore, dealing with scalability implicitly induces the heterogeneity issue [43]. According to Chapel's official documentation, the Xeon Phi accelerator is supported.…”

The Landscape of Exascale Research

“…Mittal and Vetter [129] performed an extensive survey on CPU-GPU heterogeneous computing and conclude that a collaboration between CPU and GPU is inevitable to achieve the exascale goals. To achieve exascale performance, AMD [160] envisions a heterogeneous processor (accelerated processing unit, APU) that integrates both CPUs and GPUs on the same chip. They consider such a design to be ideal for HPC since it combines the high throughput and energy-efficiency of GPUs with the fast serial processing of CPUs.…”

SAGE: Percipient Storage for Exascale Data Centric Computing

“…This capability is only made possible by having an architecture that can reasonably incorporate a wide variety of storage device technologies to meet various performance and capacity objectives at reasonable costs, as there is clearly no single storage device type that works optimally for all workloads. Further, most workloads require a variety of storage types to deliver good performance at an acceptable cost [8]. Indeed, this requirement is well recognized by the European as well as the International community.…”