DRAM row and column sparing cannot efficiently tolerate the increasing inherent fault rate caused by continued process scaling. In-DRAM ECC (IECC), an appealing alternative to sparing, can resolve inherent faults without significant changes to DRAM, but it is inefficient for highly-reliable systems where rank-level ECC (RECC) is already used against operational faults. In addition, DRAM design in the near future (possibly as early as DDR5) may transfer data in longer bursts, which complicates high-reliability RECC due to fewer devices being used per rank and increased fault granularity. We propose dual use of on-chip redundancy (DUO), a mech- anism that bypasses the IECC module and transfers on-chip redundancy to be used directly for RECC. Due to its increased redundancy budget, DUO enables a strong and novel RECC for highly-reliable systems, called DUO SDDC. The long codewords of DUO SDDC provide fundamentally higher detection and correction capabilities, and several novel secondary-correction techniques integrate together to further expand its correction capability. According to our evaluation results, DUO shows performance degradation on par with or better than IECC (average 2–3%), while consuming less DRAM energy than IECC (average 4–14% overheads). DUO provides higher reliability than either IECC or the state-of-the-art ECC technique. We show the robust reliability of DUO SDDC by comparing it to other ECC schemes using two different inherent fault-error models.

DARPA's Ubiquitous High-Performance Computing (UHPC) program asked researchers to develop computing systems capable of achieving energy efficiencies of 50 GOPS/Watt, assuming 2018-era fabrication technologies. This paper describes Runnemede, the research architecture developed by the Intel-led UHPC team. Runnemede is being developed through a co-design process that considers the hardware, the runtime/OS, and applications simultaneously. Near-threshold voltage operation, fine-grained power and clock management, and separate execution units for runtime and application code are used to reduce energy consumption. Memory energy is minimized through application-managed on-chip memory and direct physical addressing. A hierarchical on-chip network reduces communication energy, and a codelet-based execution model supports extreme parallelism and fine-grained tasks. We present an initial evaluation of Runnemede that shows the design process for our on-chip network, demonstrates 2-4x improvements in memory energy from explicit control of on-chip memory, and illustrates the impact of hardware-software co-design on the energy consumption of a synthetic aperture radar algorithm on our architecture.

The drive for higher performance and energy efficiency in data-centers has influenced trends toward increased power and cooling requirements in the facilities. Since enterprise servers rarely operate at their peak capacity, efficient power capping is deemed as a critical component of modern enterprise computing environments. In this paper we propose a new power measurement and power limiting architecture for main memory. Specifically, we describe a new approach for measuring memory power and demonstrate its applicability to a novel power limiting algorithm. We implement and evaluate our approach in the modern servers and show that we achieve up to 40% lower performance impact when compared to the state-of-art baseline across the power limiting range.

The widespread use of multicore processors has dramatically increased the demand on high memory bandwidth and large memory capacity. As DRAM subsystem designs stretch to meet the demand, memory power consumption is now approaching that of processors. However, the conventional DRAM architecture prevents any meaningful power and performance trade-offs for memory-intensive workloads. We propose a novel idea called mini-rank for DDRx (DDR/DDR2/DDR3) DRAMs, which uses a small bridge chip on each DRAM DIMM to break a conventional DRAM rank into multiple smaller mini-ranks so as to reduce the number of devices involved in a single memory access. The design dramatically reduces the memory power consumption with only a slight increase on the memory idle latency. It does not change the DDRx bus protocol and its configuration can be adapted for the best performance-power trade-offs. Our experimental results using four-core multiprogramming workloads show that using x32 mini-ranks reduces memory power by 27.0% with 2.8% performance penalty and using x16 mini-ranks reduces memory power by 44.1% with 7.4% performance penalty on average for memory-intensive workloads, respectively.

Thermal management of DRAM memory has become a critical issue for server systems. We have done, to our best knowledge, the first study of software thermal management for memory subsystem on real machines. Two recently proposed DTM (Dynamic Thermal Management) policies have been improved and implemented in Linux OS and evaluated on two multicore servers, a Dell PowerEdge 1950 server and a customized Intel SR1500AL server testbed. The experimental results first confirm that a system-level memory DTM policy may significantly improve system performance and power efficiency, compared with existing memory bandwidth throttling scheme. A policy called DTM-ACG (Adaptive Core Gating) shows performance improvement comparable to that reported previously. The average performance improvements are 13.3% and 7.2% on the PowerEdge 1950 and the SR1500AL (vs. 16.3% from the previous simulation-based study), respectively. We also have surprising findings that reveal the weakness of the previous study: the CPU heat dissipation and its impact on DRAM memories, which were ignored, are significant factors. We have observed that the second policy, called DTM-CDVFS (Coordinated Dynamic Voltage and Frequency Scaling), has much better performance than previously reported for this reason. The average improvements are 10.8% and 15.3% on the two machines (vs. 3.4% from the previous study), respectively. It also significantly reduces the processor power by 15.5% and energy by 22.7% on average.

With increasing speed and power density, high-performance memories, including FB-DIMM (Fully Buffered DIMM) and DDR2 DRAM, now begin to require dynamic thermal management(DTM) as processors and hard drives did. The DTM of memories, nevertheless, is different in that it should take the processor performance and power consumption into consideration. Existing schemes have ignored that. In this study, we investigate a new approach that controls the memory thermal issues from the source generating memory activities - the processor. It will smooth the program execution when compared with shutting down memory abruptly, and therefore improve the overall system performance and power efficiency. For multicore systems, we propose two schemes called adaptive core gating and coordinated DVFS. The first scheme activates clock gating on selected processor cores and the second one scales down the frequency and voltage levels of processor cores when the memory is to be over-heated. They can successfully control the memory activities and handle thermal emergency. More importantly, they improve performance significantly under the given thermal envelope. Our simulation results show that adaptive coregating improves performance by up to 23.3% (16.3% on average) on a four-core system with FB-DIMM when compared with DRAM thermal shutdown; and coordinated DVFS with control-theoretic methods improves the performance by up to 18.5% (8.3% on average).

We have studied DRAM-level prefetching for the fully buffered DIMM (FB-DIMM) designed for multi-core processors. FB-DIMM has a unique two-level interconnect structure, with FB-DIMM channels at the first-level connecting the memory controller and Advanced Memory Buffers (AMBs); and DDR2 buses at the second-level connecting the AMBs with DRAM chips. We propose an AMB prefetching method that prefetches memory blocks from DRAM chips to AMBs. It utilizes the redundant bandwidth between the DRAM chips and AMBs but does not consume the crucial channel bandwidth. The proposed method fetches K memory blocks of L2 cache block sizes around the demanded block, where K is a small value ranging from two to eight. The method may also reduce the DRAM power consumption by merging some DRAM precharges and activations. Our cycle-accurate simulation shows that the average performance improvement is 16% for single-core and multi-core workloads constructed from memory-intensive SPEC2000 programs with software cache prefetching enabled; and no workload has negative speedup. We have found that the performance gain comes from the reduction of idle memory latency and the improvement of channel bandwidth utilization. We have also found that there is only a small overlap between the performance gains from the AMB prefetching and the software cache prefetching. The average of estimated power saving is 15%.