摘要:
Collectively loading an application in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: identifying, by a parallel computer control system, a subset of compute nodes in the parallel computer to execute a job; selecting, by the parallel computer control system, one of the subset of compute nodes in the parallel computer as a job leader compute node; retrieving, by the job leader compute node from computer memory, an application for executing the job; and broadcasting, by the job leader to the subset of compute nodes in the parallel computer, the application for executing the job.
摘要:
A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaOPS-scale computing, at decreased cost, power and footprint, and that allows for a maximum packaging density of processing nodes from an interconnect point of view. The Supercomputer exploits technological advances in VLSI that enables a computing model where many processors can be integrated into a single Application Specific Integrated Circuit (ASIC). Each ASIC computing node comprises a system-on-chip ASIC utilizing four or more processors integrated into one die, with each having full access to all system resources and enabling adaptive partitioning of the processors to functions such as compute or messaging I/O on an application by application basis, and preferably, enable adaptive partitioning of functions in accordance with various algorithmic phases within an application, or if I/O or other processors are underutilized, then can participate in computation or communication nodes are interconnected by a five dimensional torus network with DMA that optimally maximize the throughput of packet communications between nodes and minimize latency.
摘要翻译:具有100 petaOPS规模计算的多Petascale高效并行超级计算机,其成本,功耗和占地面积都在降低,并且允许从互连角度来看处理节点的最大封装密度。 超级计算机利用了VLSI的技术进步,实现了许多处理器可以集成到单个专用集成电路(ASIC)中的计算模型。 每个ASIC计算节点包括利用集成到一个管芯中的四个或更多个处理器的片上系统ASIC,每个处理器具有对所有系统资源的完全访问,并且使得处理器能够对诸如计算或消息传递I / O 并且优选地,根据应用内的各种算法阶段实现功能的自适应分割,或者如果I / O或其他处理器未被充分利用,则可以参与计算或通信节点通过五维环面网络互连 使用DMA来最大限度地最大化节点之间的分组通信的吞吐量并最小化等待时间。
摘要:
Collectively loading an application in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: identifying, by a parallel computer control system, a subset of compute nodes in the parallel computer to execute a job; selecting, by the parallel computer control system, one of the subset of compute nodes in the parallel computer as a job leader compute node; retrieving, by the job leader compute node from computer memory, an application for executing the job; and broadcasting, by the job leader to the subset of compute nodes in the parallel computer, the application for executing the job.
摘要:
Messaging in a parallel computer using remote direct memory access (‘RDMA’), including: receiving a send work request; responsive to the send work request: translating a local virtual address on the first node from which data is to be transferred to a physical address on the first node from which data is to be transferred from; creating a local RDMA object that includes a counter set to the size of a messaging acknowledgment field; sending, from a messaging unit in the first node to a messaging unit in a second node, a message that includes a RDMA read operation request, the physical address of the local RDMA object, and the physical address on the first node from which data is to be transferred from; and receiving, by the first node responsive to the second node's execution of the RDMA read operation request, acknowledgment data in the local RDMA object.
摘要:
Executing MIMD programs on a SIMD machine, the SIMD machine including a plurality of compute nodes, each compute node capable of executing only a single thread of execution, the compute nodes initially configured exclusively for SIMD operations, the SIMD machine further comprising a data communications network, the network comprising synchronous data communications links among the compute nodes, including establishing one or more SIMD partitions, booting one or more SIMD partitions in MIMD mode; establishing a MIMD partition; executing by launcher programs a plurality of MIMD programs on two or more of the compute nodes of the MIMD partition; and re-executing a launcher program by an operating system on a compute node in the MIMD partition upon termination of the MIMD program executed by the launcher program.
摘要:
Executing Multiple Instructions Multiple Data (‘MIMD’) programs on a Single Instruction Multiple Data (‘SIMD’) machine, the SIMD machine including a plurality of compute nodes, each compute node capable of executing only a single thread of execution, the compute nodes initially configured exclusively for SIMD operations, the SIMD machine further comprising a data communications network, the network comprising synchronous data communications links among the compute nodes, including establishing a SIMD partition comprising a plurality of the compute nodes; booting the SIMD partition in MIMD mode; executing by launcher programs a plurality of MIMD programs on compute nodes in the SIMD partition; and re-executing a launcher program by an operating system on a compute node in the SIMD partition upon termination of the MIMD program executed by the launcher program.