اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدAmber: Enabling Precise Full-System Simulation with Detailed Modeling of All SSD Resources
91
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
SSDs become a major storage component in modern memory hierarchies, and SSD research demands exploring future simulation-based studies by integrating SSD subsystems into a full-system environment. However, several challenges exist to model SSDs under a full-system simulations; SSDs are composed upon their own complete system and architecture, which employ all necessary hardware, such as CPUs, DRAM and interconnect network. Employing the hardware components, SSDs also require to have multiple device controllers, internal caches and software modules that respect a wide spectrum of storage interfaces and protocols. These SSD hardware and software are all necessary to incarnate storage subsystems under full-system environment, which can operate in parallel with the host system. In this work, we introduce a new SSD simulation framework, SimpleSSD 2.0, namely Amber, that models embedded CPU cores, DRAMs, and various flash technologies (within an SSD), and operate under the full system simulation environment by enabling a data transfer emulation. Amber also includes full firmware stack, including DRAM cache logic, flash firmware, such as FTL and HIL, and obey diverse standard protocols by revising the host DMA engines and system buses of a popular full system simulators all functional and timing CPU models (gem5). The proposed simulator can capture the details of dynamic performance and power of embedded cores, DRAMs, firmware and flash under the executions of various OS systems and hardware platforms. Using Amber, we characterize several system-level challenges by simulating different types of fullsystems, such as mobile devices and general-purpose computers, and offer comprehensive analyses by comparing passive storage and active storage architectures.
قيم البحث
اقرأ أيضاً
It has always been difficult to balance the accuracy and performance of ISSs. RTL simulators or systems such as gem5 are used to execute programs in a cycle-accurate manner but are often prohibitively slow. In contrast, functional simulators such as
QEMU can run large benchmarks to completion in a reasonable time yet capture few performance metrics and fail to model complex interactions between multiple cores. This paper presents a novel multi-purpose simulator that exploits binary translation to offer fast cycle-level full-system simulations. Its functional simulation mode outperforms QEMU and, if desired, it is possible to switch between functional and timing modes at run-time. Cycle-level simulations of RISC-V multi-core processors are possible at more than 20 MIPS, a useful middle ground in terms of accuracy and performance with simulation speeds nearly 100 times those of more detailed cycle-accurate models.
A foundational goal of the Large Synoptic Survey Telescope (LSST) is to map the Solar System small body populations that provide key windows into understanding of its formation and evolution. This is especially true of the populations of the Outer So
lar System -- objects at the orbit of Neptune $r > 30$AU and beyond. In this whitepaper, we propose a minimal change to the LSST cadence that can greatly enhance LSSTs ability to discover faint distant Solar System objects across the entire wide-fast-deep (WFD) survey area. Specifically, we propose that the WFD cadence be constrained so as to deliver least one sequence of $gtrsim 10$ visits per year taken in a $sim 10$ day period in any combination of $g, r$, and $i$ bands. Combined with advanced shift-and-stack algorithms (Whidden et al. 2019) this modification would enable a nearly complete census of the outer Solar System to $sim 25.5$ magnitude, yielding $4-8$x more KBO discoveries than with single-epoch baseline, and enabling rapid identification and follow-up of unusual distant Solar System objects in $gtrsim 5$x greater volume of space. These increases would enhance the science cases discussed in Schwamb et al. (2018) whitepaper, including probing Neptunes past migration history as well as discovering hypothesized planet(s) beyond the orbit of Neptune (or at least placing significant constraints on their existence).
With the goal of gaining a deeper understanding of quantum non-locality, we decompose quantum correlations into more elementary non-local correlations. We show that the correlations of all pure entangled states of two qubits can be simulated without
communication, hence using only non-signaling resources. Our simulation model works in two steps. First, we decompose the quantum correlations into a local and a non-local part. Second, we present a model for simulating the nonlocal part using only non-signaling resources. In our model partially entangled states require more nonlocal resources than maximally entangled states, but the less the state is entangled, the less frequently must the nonlocal resources be used.
An exponential growth in data volume, combined with increasing demand for real-time analysis (i.e., using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid
transactional and analytical processing (HTAP) database systems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant drops in transactional (up to 74.6%) and/or analytical (up to 49.8%) throughput compared to performing only transactions or only analytics in isolation, due to (1) data movement between the CPU and memory, (2) data update propagation, and (3) consistency costs. We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases. Polynesia (1) divides the HTAP system into transactional and analytical processing islands, (2) implements custom algorithms and hardware to reduce the costs of update propagation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement. Our evaluation shows that Polynesia outperforms three state-of-the-art HTAP systems, with average transactional/analytical throughput improvements of 1.70X/3.74X, and reduces energy consumption by 48% over the prior lowest-energy system.
We have developed a detailed Monte Carlo simulation program for the Belle TOF system. Based on GEANT simulation, it takes account of all physics processes in the TOF scintillation counters and readout electronics. The simulation reproduces very well
the performance of the Belle TOF system, including the dE/dx response, the time walk effect, the time resolution, and the hit efficiency due to beam background. In this report, we will describe the Belle TOF simulation program in detail.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
