We expect that many-core microprocessors will push performance per chip from the 10 gigaflop to the 10 teraflop range in the coming decade. To support this increased performance, memory and inter-core bandwidths will also have to scale by orders of magnitude. Pin limitations, the energy cost of electrical signaling, and the non-scalability of chip-length global wires are significant bandwidth impediments. Recent developments in silicon nanophotonic technology have the potential to meet these off- and on-stack bandwidth requirements at acceptable power levels. Corona is a 3D many-core architecture that uses nanophotonic communication for both inter-core communication and off-stack communication to memory or I/O devices. Its peak floating-point performance is 10 teraflops. Dense wavelength division multiplexed optically connected memory modules provide 10 terabyte per second memory bandwidth. A photonic crossbar fully interconnects its 256 low-power multithreaded cores at 20 terabyte per second bandwidth. We have simulated a 1024 thread Corona system running synthetic benchmarks and scaled versions of the SPLASH-2 benchmark suite. We believe that in comparison with an electrically-connected many-core alternative that uses the same on-stack interconnect power, Corona can provide 2 to 6 times more performance on many memory intensive workloads, while simultaneously reducing power.

/pdf/corona-system-implications-of-emerging-nanophotonic-1p6apqmj51.pdf

Corona: System Implications of Emerging Nanophotonic Technology

This paper introduces the Graphite open-source distributed parallel multicore simulator infrastructure. Graphite is designed from the ground up for exploration of future multi-core processors containing dozens, hundreds, or even thousands of cores. It provides high performance for fast design space exploration and software development. Several techniques are used to achieve this including: direct execution, seamless multicore and multi-machine distribution, and lax synchronization. Graphite is capable of accelerating simulations by distributing them across multiple commodity Linux machines. When using multiple machines, it provides the illusion of a single process with a single, shared address space, allowing it to run off-the-shelf pthread applications with no source code modification. Our results demonstrate that Graphite can simulate target architectures containing over 1000 cores on ten 8-core servers. Performance scales well as more machines are added with near linear speedup in many cases. Simulation slowdown is as low as 41× versus native execution.

/pdf/graphite-a-distributed-parallel-simulator-for-multicores-1y50n9y9w6.pdf

Graphite: A distributed parallel simulator for multicores

Analysis of technology and application trends reveals a growing imbalance in the peak compute-to-memory-capacity ratio for future servers. At the same time, the fraction contributed by memory systems to total datacenter costs and power consumption during typical usage is increasing. In response to these trends, this paper re-examines traditional compute-memory co-location on a single system and details the design of a new general-purpose architectural building block-a memory blade-that allows memory to be "disaggregated" across a system ensemble. This remote memory blade can be used for memory capacity expansion to improve performance and for sharing memory across servers to reduce provisioning and power costs. We use this memory blade building block to propose two new system architecture solutions-(1) page-swapped remote memory at the virtualization layer, and (2) block-access remote memory with support in the coherence hardware-that enable transparent memory expansion and sharing on commodity-based systems. Using simulations of a mix of enterprise benchmarks supplemented with traces from live datacenters, we demonstrate that memory disaggregation can provide substantial performance benefits (on average 10X) in memory constrained environments, while the sharing enabled by our solutions can improve performance-per-dollar by up to 57% when optimizing memory provisioning across multiple servers.

/pdf/disaggregated-memory-for-expansion-and-sharing-in-blade-yf0xv0kfvz.pdf

Disaggregated memory for expansion and sharing in blade servers

In this paper we introduce CACTI-D, a significant enhancement of CACTI 5.0. CACTI-D adds support for modeling of commodity DRAM technology and support for main memory DRAM chip organization. CACTI-D enables modeling of the complete memory hierarchy with consistent models all the way from SRAM based L1 caches through main memory DRAMs on DIMMs. We illustrate the potential applicability of CACTI-D in the design and analysis of future memory hierarchies by carrying out a last level cache study for a multicore multithreaded architecture at the 32nm technology node. In this study we use CACTI-D to model all components of the memory hierarchy including L1, L2, last level SRAM, logic process based DRAM or commodity DRAM L3 caches, and main memory DRAM chips. We carry out architectural simulation using benchmarks with large data sets and present results of their execution time, breakdown of power in the memory hierarchy, and system energy-delay product for the different system configurations. We find that commodity DRAM technology is most attractive for stacked last level caches, with significantly lower energy-delay products.

A Comprehensive Memory Modeling Tool and Its Application to the Design and Analysis of Future Memory Hierarchies

Technology trends may soon favor building main memory as a hybrid between DRAM and non-volatile memory, such as flash or PC-RAM. We describe how the operating system might manage such hybrid memories, using semantic information not available in other layers. We describe preliminary experiments suggesting that this approach is viable.

/pdf/operating-system-support-for-nvm-dram-hybrid-main-memory-a56f5s9h84.pdf

Operating system support for NVM+DRAM hybrid main memory

The computing industry is changing rapidly, pushing strongly to consolidation into large “cloud computing” datacenters. New power, availability, and cost constraints require installations that are better optimized for their intended use. The problem of right-sizing large datacenters requires tools that can characterize both the target workloads and the hardware architecture space. Together with the resurgence of variety in industry standard CPUs, driven by very ambitious multi-core roadmaps, this is making the existing modeling techniques obsolete. In this chapter we revisit the basic computer architecture simulation concepts toward enabling fast and reliable datacenter simulation. Speed, full system, and modularity are the fundamental characteristics of a datacenter-level simulator. Dynamically trading off speed/accuracy, running an unmodified software stack, and leveraging existing “component” simulators are some of the key aspects that should drive next generation simulator’s design. As a case study, we introduce the COTSon simulation infrastructure, a scalable full-system simulator developed by HP Labs and AMD, targeting fast and accurate evaluation of current and future computing systems.

Toward the Datacenter: Scaling Simulation Up and Out

Computer clusters are a very cost–effective approach for High Performance Computing, but simulating a complete cluster is still an open research problem. The obvious approach—to parallelize individual node simulators—is complex and slow. Combining individual parallel simulators implies synchronizing their progress of time. This can be accomplished with a variety of parallel discrete event simulation techniques, but unfortunately any straightforward approach introduces a synchronization overhead causing up two orders of magnitude of slowdown with respect to the simulation speed of an individual node. In this paper we present a novel adaptive technique that automatically adjusts the synchronization boundaries. By dynamically relaxing accuracy over the least interesting computational phases we dramatically increase performance with a marginal loss of precision. For example, in the simulation of an 8-node cluster running NAMD (a parallel molecular dynamics application) we show an acceleration factor of 26x over the deterministic “ground truth” simulation, at less than a 1% accuracy error.

Ayose Falcón

Papers

Toward the Datacenter: Scaling Simulation Up and Out

AnAdaptiveSynchronizationTechniquefor P arallelSimulationofNetworkedClusters