Whenever I speak about Intel Labs’ Tera-scale Computing Research program I face skepticism about how far the growth to multiple cores can extend. “How will anyone ever use 100′s of cores on one task?” sums up the typical comments. Coordinating the complex interactions among cores will limit the benefit to a few cores, even if it can be done without errors. How? We have plenty of examples in the world of “scale-out” computing in the “Cloud” where routinely thousands of nodes are used for everything from search to machine learning. “Scale-out” refers to using more independent nodes to tackle an application, each with their own memory, as opposed to “scale-up” where more resources (cores, memory) are added to a node with shared memory. Messaging among nodes replaces coordinated sharing of the same memory.Today Intel Labs disclosed our next-generation many-core research prototype processor, the Single chip Cloud Computer (SCC). It is designed to allow exploration of scale-out programming or “Cloud computing” on-die. The SCC has 48 Intel-Architecture (IA) core, the largest number ever put on a CPU, connected by an on-die network with latency and bandwidth (256GB/sec) only dreamed of by designers of traditional clusters. SCC is both a microcosm of a Cloud datacenter and an example of a possible architecture for a future many-core datacenter processor that exploits the efficiencies of high-integration silicon design. Power management is also a focus, providing fine-grained software control off voltage and frequency. SCC can run all 48 cores at a time over a range of 25 to 125W, or selectively vary to voltage and frequency of the mesh and sets of cores. The super-computing community has used massive parallelism for years, with message-passing programming models based on APIs such as MPI. The newest generation of super-computers use huge numbers of ordinary processors connected in high performance networks as the computer with the same programming model – with proven scaling to thousands of nodes. Cloud computing centers have moved to the same architecture with greatly simplified programming models such as Google’s MapReduce that abstract the messaging from the programmer – applying thousands of processors on large data sets to problems such as sorting, machine learning and statistical machine translation. What might we apply to parallel programming for mainstream desktop and laptop processors from what has been learned in these areas? We believe SCC is an ideal test-bed to explore parallel programming approaches for the mainstream as well as how the Cloud computing performance could be improved with an on-die architecture that reflects the larger Cloud. Microsoft Research is demonstrating how Visual Studio can be modified for development of a message passing application on SCC. Intel Labs has demonstrating the implementation of a microcosm of a Cloud datacenter on chip, with Linux on each core, running an application using Hadoop. We’re also showing demonstrations of message-passing as well as of software-based coherent shared memory SCC. We expect to make SCC available to academic and industry partners (SCC software research program) who will explore all of these directions and more, leading to understanding of how to build better processors for the Cloud as well as how scalable programming models and architectures that will apply to all processors for all market segments. Today we enter an exciting new phase of our research to bring exciting new applications to uses with the performance of many-core processors. Releated blog: Watch the livecast from the event
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