International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

A cooperative control paradigm is used to establish a distributed secondary/primary control framework for dc microgrids. The conventional secondary control, that adjusts the voltage set point for the local droop mechanism, is replaced by a voltage regulator and a current regulator. A noise-resilient voltage observer is introduced that uses neighbors’ data to estimate the average voltage across the microgrid. The voltage regulator processes this estimation and generates a voltage correction term to adjust the local voltage set point. This adjustment maintains the microgrid voltage level as desired by the tertiary control. The current regulator compares the local per-unit current of each converter with the neighbors’ and, accordingly, provides a second voltage correction term to synchronize per-unit currents and, thus, provide proportional load sharing. The proposed controller precisely handles the transmission line impedances. The controller on each converter communicates with only its neighbor converters on a communication graph. The graph is a sparse network of communication links spanned across the microgrid to facilitate data exchange. The global dynamic model of the microgrid is derived, and design guidelines are provided to tune the system's dynamic response. A low-voltage dc microgrid prototype is set up, where the controller performance, noise resiliency, link-failure resiliency, and the plug-and-play capability features are successfully verified.

Distributed Cooperative Control of DC Microgrids

We present here a report produced by a workshop on 'Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach.

The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.

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Addressing failures in exascale computing

This paper presents CMP Cooperative Caching, a unified framework to manage a CMP's aggregate on-chip cache resources. Cooperative caching combines the strengths of private and shared cache organizations by forming an aggregate "shared" cache through cooperation among private caches. Locally active data are attracted to the private caches by their accessing processors to reduce remote on-chip references, while globally active data are cooperatively identified and kept in the aggregate cache to reduce off-chip accesses. Examples of cooperation include cache-to-cache transfers of clean data, replication-aware data replacement, and global replacement of inactive data. These policies can be implemented by modifying an existing cache replacement policy and cache coherence protocol, or by the new implementation of a directory-based protocol presented in this paper. Our evaluation using full-system simulation shows that cooperative caching achieves an off-chip miss rate similar to that of a shared cache, and a local cache hit rate similar to that of using private caches. Cooperative caching performs robustly over a range of system/cache sizes and memory latencies. For an 8-core CMP with 1MB L2 cache per core, the best cooperative caching scheme improves the performance of multithreaded commercial workloads by 5-11% compared with a shared cache and 4-38% compared with private caches. For a 4-core CMP running multiprogrammed SPEC2000 workloads, cooperative caching is on average 11% and 6% faster than shared and private cache organizations, respectively.

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Cooperative Caching for Chip Multiprocessors

In deep sub-micron ICs, growing amounts of on-die memory and scaling effects make embedded memories increasingly vulnerable to reliability and yield problems. As scaling progresses, soft and hard errors in the memory system will increase and single error events are more likely to cause large-scale multi- bit errors. However, conventional memory protection techniques can neither detect nor correct large-scale multi-bit errors without incurring large performance, area, and power overheads. We propose two-dimensional (2D) error coding in embedded memories, a scalable multi-bit error protection technique to improve memory reliability and yield. The key innovation is the use of vertical error coding across words that is used only for error correction in combination with conventional per-word horizontal error coding. We evaluate this scheme in the cache hierarchies of two representative chip multiprocessor designs and show that 2D error coding can correct clustered errors up to 32times32 bits with significantly smaller performance, area, and power overheads than conventional techniques.

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Multi-bit Error Tolerant Caches Using Two-Dimensional Error Coding

Several recent publications have shown that hardware faults in the memory subsystem are commonplace. These faults are predicted to become more frequent in future systems that contain orders of magnitude more DRAM and SRAM than found in current memory subsystems. These memory subsystems will need to provide resilience techniques to tolerate these faults when deployed in high-performance computing systems and data centers containing tens of thousands of nodes. Therefore, it is critical to understand the efficacy of current hardware resilience techniques to determine whether they will be suitable for future systems. In this paper, we present a study of DRAM and SRAM faults and errors from the field. We use data from two leadership-class high-performance computer systems to analyze the reliability impact of hardware resilience schemes that are deployed in current systems. Our study has several key findings about the efficacy of many currently deployed reliability techniques such as DRAM ECC, DDR address/command parity, and SRAM ECC and parity. We also perform a methodological study, and find that counting errors instead of faults, a common practice among researchers and data center operators, can lead to incorrect conclusions about system reliability. Finally, we use our data to project the needs of future large-scale systems. We find that SRAM faults are unlikely to pose a significantly larger reliability threat in the future, while DRAM faults will be a major concern and stronger DRAM resilience schemes will be needed to maintain acceptable failure rates similar to those found on today's systems.

Memory Errors in Modern Systems: The Good, The Bad, and The Ugly

Most modern computer systems use dynamic random access memory (DRAM) as a main memory store. Recent publications have confirmed that DRAM errors are a common source of failures in the field. Therefore, further attention to the faults experienced by DRAM sub-systems is warranted. In this paper, we present a study of 11 months of DRAM errors in a large high-performance computing cluster. Our goal is to understand the failure modes, rates, and fault types experienced by DRAM in production settings. We identify several unique DRAM failure modes, including single-bit, multi-bit, and multi-chip failures. We also provide a deterministic bound on the rate of transient faults in the DRAM array, by exploiting the presence of a hardware scrubber on our nodes. We draw several conclusions from our study. First, DRAM failures are dominated by permanent, rather than transient, faults, although not to the extent found by previous publications. Second, DRAMs are susceptible to large multi-bit failures, such as failures that affect an entire DRAM row or column, indicating faults in shared internal circuitry. Third, we identify a DRAM failure mode that disrupts access to other DRAM devices that share the same board-level circuitry. Finally, we find that chipkill error-correcting codes (ECC) are extremely effective, reducing the node failure rate from uncorrected DRAM errors by 42x compared to single-error correct/double-error detect (SEC-DED) ECC.

A study of DRAM failures in the field

Several recent publications confirm that faults are common in high-performance computing systems. Therefore, further attention to the faults experienced by such computing systems is warranted. In this paper, we present a study of DRAM and SRAM faults in large high-performance computing systems. Our goal is to understand the factors that influence faults in production settings. We examine the impact of aging on DRAM, finding a marked shift from permanent to transient faults in the first two years of DRAM lifetime. We examine the impact of DRAM vendor, finding that fault rates vary by more than 4x among vendors. We examine the physical location of faults in a DRAM device and in a data center; contrary to prior studies, we find no correlations with either. Finally, we study the impact of altitude and rack placement on SRAM faults, finding that, as expected, altitude has a substantial impact on SRAM faults, and that top of rack placement correlates with 20% higher fault rate.

Feng shui of supercomputer memory: positional effects in DRAM and SRAM faults

The Architectural Vulnerability Factor (AVF) of a hardware structure is the probability that a fault in the structure will affect the output of a program. AVF captures both microarchitectural and architectural fault masking effects; therefore, AVF measurements cannot generate insight into the vulnerability of software independent of hardware. To evaluate the behavior of software in the presence of hardware faults, we must isolate the software-dependent (architecture-level masking) portion of AVF from the hardware-dependent (microarchitecture-level masking) portion, providing a quantitative basis to make reliability decisions about software independent of hardware. In this work, we demonstrate that the new Program Vulnerability Factor (PVF) metric provides such a basis: PVF captures the architecture-level fault masking inherent in a program, allowing software designers to make quantitative statements about a program's tolerance to soft errors. PVF can also explain the AVF behavior of a program when executed on hardware; PVF captures the workload-driven changes in AVF for all structures. Finally, we demonstrate two practical uses for PVF: choosing algorithms and compiler optimizations to reduce a program's failure rate.

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Eliminating microarchitectural dependency from Architectural Vulnerability

Cosmic-ray induced soft errors in cache memories are becoming a major threat to the reliability of microprocessor-based systems. In this paper, we present a new method to accurately estimate the reliability of cache memories. We have measured the MTTF (mean-time-to-failure) of unprotected first-level (L1) caches for twenty programs taken from SPEC2000 benchmark suite. Our results show that a 16 KB first-level cache possesses a MTTF of at least 400 years (for a raw error rate of 0.002 FIT/bit.) However, this MTTF is significantly reduced for higher error rates and larger cache sizes. Our results show that for selected programs, a 64 KB first-level cache is more than 10 times as vulnerable to soft errors versus a 16 KB cache memory. Our work also illustrates that the reliability of cache memories is highly application-dependent. Finally, we present three different techniques to reduce the susceptibility of first-level caches to soft errors by two orders of magnitude. Our analysis shows how to achieve a balance between performance and reliability

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Vilas Sridharan

Papers

Memory Errors in Modern Systems: The Good, The Bad, and The Ugly

A study of DRAM failures in the field

Feng shui of supercomputer memory: positional effects in DRAM and SRAM faults

Eliminating microarchitectural dependency from Architectural Vulnerability

Balancing Performance and Reliability in the Memory Hierarchy