Decentralized Federated Learning (DFL) enables collaborative training across distributed nodes without central coordination, but it also exposes new attack surfaces: a single compromised node can poison updates and degrade global performance. In this paper, we demonstrate Quantization-aware Distortion (QuAD) for DFL, a model-poisoning attack where a malicious model appears benign in full precision but triggers harmful behavior after quantization. To mitigate this threat, we propose EnsembleHealer, a decentralized recovery framework that detects and isolates poisoned or degraded models through latency-aware distributed agreement. EnsembleHealer combines application-level constraints and dynamic quorum selection to detect and isolate compromised model copies in a fully decentralized setting. Our experiments show that QuAD can reduce the federation’s overall accuracy by 9% even with a single compromised node. EnsembleHealer efficiently identifies the compromised nodes by cutting the costs involved in agreement building between the nodes by 50%, and improves packet-loss recovery by 10 × relative to the Paxos algorithm.

In recent years, several hardware-based countermeasures proposed to mitigate Spectre attacks have been shown to be insecure. To enable the development of effective secure speculation countermeasures, we need easy-to-use tools that can automatically test their security guarantees early-on in the design phase to facilitate rapid prototyping.

It is our pleasure to present the technical program for MICRO 2024, the 57th ACM/IEEE International Symposium on Microarchitecture. This year's program features a wide variety of exciting topics representing the state of the art in computer architecture research. We had a record 497 submissions, an increase of 15& over last year. We accepted a record 113 papers. The reviewing process included 152 program committee (PC) members, 176 external review committee (ERC) members, and few other expert reviewers.

Recent security vulnerabilities that target speculative execution (e.g., Spectre) present a significant challenge for processor design. These highly publicized vulnerabilities use speculative execution to learn victim secrets by changing the cache state. As a result, recent computer architecture research has focused on invisible speculation mechanisms that attempt to block changes in cache state due to speculative execution. Prior work has shown significant success in preventing Spectre and other attacks at modest performance costs. In this paper, we introduce speculative interference attacks, which show that prior invisible speculation mechanisms do not fully block speculation-based attacks that use cache state. We make two key observations. First, mis-speculated younger instructions can change the timing of older, bound-to-retire instructions, including memory operations. Second, changing the timing of a memory operation can change the order of that memory operation relative to other memory operations, resulting in persistent changes to the cache state. Using both of these observations, we demonstrate (among other attack variants) that secret information accessed by mis-speculated instructions can change the order of bound-to-retire loads. Load timing changes can therefore leave secret-dependent changes in the cache, even in the presence of invisible speculation mechanisms. We show that this problem is not easy to fix. Speculative interference converts timing changes to persistent cache-state changes, and timing is typically ignored by many cache-based defenses. We develop a framework to understand the attack and demonstrate concrete proof-of-concept attacks against invisible speculation mechanisms. We conclude with a discussion of security definitions that are sufficient to block the attacks, along with preliminary defense ideas based on those definitions.

Streaming graphs are ubiquitous in today’s big data era. Prior work has improved the performance of streaming graph workloads without taking input characteristics into account. In this work, we demonstrate that input knowledge-driven software and hardware co-design is critical to optimize the performance of streaming graph processing. To improve graph update efficiency, we first characterize the performance trade-offs of input-oblivious batch reordering. Guided by our findings, we propose input-aware batch reordering to adaptively reorder input batches based on their degree distributions. To complement adaptive batch reordering, we propose updating graphs dynamically, based on their input characteristics, either in software (via update search coalescing) or in hardware (via acceleration support). To improve graph computation efficiency, we present input-aware work aggregation which adaptively modulates the computation granularity based on inter-batch locality characteristics. Evaluated across 260 workloads, our input-aware techniques provide on average 4.55 × and 2.6 × improvement in graph update performance for different input types (on top of eliminating the performance degradation from input-oblivious batch reordering). The graph compute performance is improved by 1.26 × (up to 2.7 ×).

The memory system is vulnerable to a number of security breaches, e.g., an attacker can interfere with program execution by disrupting values stored in memory. Modern Intel® Software Guard Extension (SGX) systems already support integrity trees to detect such malicious behavior. However, in spite of recent innovations, the bandwidth overhead of integrity+replay protection is non-trivial; state-of-the-art solutions like Synergy introduce average slowdowns of 2.3× for memory-intensive benchmarks. Prior work also implements a tree that is shared by multiple applications, thus introducing a potential side channel. In this work, we build on the Synergy and SGX baselines, and introduce three new techniques. First, we isolate each application by implementing a separate integrity tree and metadata cache for each application; this improves metadata cache efficiency and improves performance by 39%, while eliminating the potential side channel. Second, we reduce the footprint of the metadata. Synergy uses a combination of integrity and error correction metadata to provide low-overhead support for both. We share error correction metadata across multiple blocks, thus lowering its footprint (by 16×) while preventing error correction only in rare corner cases. However, we discover that shared error correction metadata, even with caching, does not improve performance. Third, we observe that thanks to its lower footprint, the error correction metadata can be embedded into the integrity tree. This reduces the metadata blocks that must be accessed to support both integrity verification and chipkill reliability. The proposed Isolated Tree with Embedded Shared Parity (ITESP) yields an overall performance improvement of 64%, relative to baseline Synergy.

Many application scenarios such as social network analysis and real-time financial fraud detection involve performing batched updates and analytics on a time-evolving or streaming graph. Despite their importance, streaming graph analytics workloads have not been systematically studied at either the software or the architecture levels. This paper fills this gap through three contributions. First, we develop and open-source SAGA-Bench, a benchmark for streaming graph analytics, which puts together different data structures and compute models on the same platform for a fair and systematic characterization. Second, we perform software-level characterization using SAGA-Bench. Our profiling reveals that the best data structure for a streaming graph depends on the per-batch degree distribution of the graph. We also observe that the incremental compute model provides performance benefits especially for larger graphs. Finally, we show that the graph update phase contributes at least 40% of the streaming graph processing latency in many cases. Third, we perform workload characterization at the architecture level. Our study reveals that the graph update phase exhibits lower utilization of architecture resources than the compute phase. Furthermore, the hardware resource utilization of the update phase strongly depends on the underlying structure of the batches of the graph. Finally, between compute and update phases, the former exhibits a higher L3 cache hit ratio, whereas the latter shows a higher L2 cache hit ratio.

Deep Neural Networks (DNNs) are becoming the prevalent approach in computer vision, machine learning, natural language processing, and speech recognition applications. Although DNNs are perceived as compute-intensive tasks, they also apply intense pressure on the capacity and bandwidth of the memory hierarchy, primarily due to the large intermediate data communicated across network layers. Prior work on hardware DNN accelerators leverages the cross-layer data sparsity via fully-customized datapaths. However, dynamically compressing/expanding such data is a challenging task for general-purpose multi-processors with virtual memory and hardware-managed coherent cache hierarchies.

Today, larger memory capacity and higher memory bandwidth are required for better performance and energy efficiency for many important client and datacenter applications. Hardware memory compression provides a promising direction to achieve this without increasing system cost. Unfortunately, current memory compression solutions face two significant challenges. First, keeping memory compressed requires additional memory accesses, sometimes on the critical path, which can cause performance overheads. Second, they require changing the operating system to take advantage of the increased capacity, and to handle incompressible data, which delays deployment. We propose Compresso, a hardware memory compression architecture that minimizes memory overheads due to compression, with no changes to the OS. We identify new data-movement trade-offs and propose optimizations that reduce additional memory movement to improve system efficiency. We propose a holistic evaluation for compressed systems. Our results show that Compresso achieves a 1.85x compression for main memory on average, with a 24% speedup over a competitive hardware compressed system for single-core systems and 27% for multi-core systems. As compared to competitive compressed systems, Compresso not only reduces performance overhead of compression, but also increases performance gain from higher memory capacity.

Modern computing systems and applications have growing demand for memories with higher bandwidth. This demand can be alleviated using fast, large on-die or die-stacked memories. They are typically used with traditional DRAM as part of a heterogeneous memory system and used either as a DRAM cache or as a hardware-or OS-managed part of memory (PoM). Caches adapt rapidly to application needs and typically provide higher performance but reduce the total OS-visible memory capacity. PoM architectures increase the total OS-visible memory capacity but exhibit additional overheads due to swapping large blocks of data between fast and slow memory. In this paper, we propose Chameleon, a hybrid architecture that bridges the gap between cache and PoM architectures. When applications need a large memory, Chameleon uses both fast and slow memories as PoM, maximizing the available space for the application. When the application's footprint is smaller than the total physical memory capacity, Chameleon opportunistically uses free space in the system as a hardware-managed cache. Chameleon is a hardware-software co-designed system where the OS notifies the hardware of pages that are allocated or freed, and hardware decides on switching memory regions between PoM-and cache-modes dynamically. Based on our evaluation of multi-programmed workloads on a system with 4GB fast memory and 20GB slow memory, Chameleon improves the average performance by 11.6% over PoM and 24.2% over a latency-optimized cache.

DRAM cells in close proximity can fail depending on the data content in neighboring cells. These failures are called data-dependent failures. Detecting and mitigating these failures online, while the system is running in the field, enables various optimizations that improve reliability, latency, and energy efficiency of the system. For example, a system can improve performance and energy efficiency by using a lower refresh rate for most cells and mitigate the failing cells using higher refresh rates or error correcting codes. All these system optimizations depend on accurately detecting every possible data-dependent failure that could occur with any content in DRAM. Unfortunately, detecting all data-dependent failures requires the knowledge of DRAM internals specific to each DRAM chip. As internal DRAM architecture is not exposed to the system, detecting data-dependent failures at the system-level is a major challenge.

The memory wall has motivated many enhancements to cache management policies aimed at reducing misses. Cache compression has been proposed to increase effective cache capacity, which potentially reduces capacity and conflict misses. However, complexity in cache compression implementations could increase cache power and access latency. On the other hand, advanced cache replacement mechanisms use heuristics to reduce misses, leading to significant performance gains. Both cache compression and replacement policies should collaborate to improve performance. In this paper, we demonstrate that cache compression and replacement policies can interact negatively. In many workloads, performance gains from replacement policies are lost due to the need to alter the replacement policy to accommodate compression. This leads to sub-optimal replacement policies that could lose performance compared to an uncompressed cache. We introduce a novel, opportunistic cache compression mechanism, Base-Victim, based on an efficient cache design. Our compression architecture improves performance on top of advanced cache replacement policies, and guarantees a hit rate at least as high as that of an uncompressed cache. For cache-sensitive applications, Base-Victim achieves an average 7.3% performance gain for single-threaded workloads, and 8.7% gain for four-thread multi-program workload mixes.

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR (double data rate) DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire DRAM rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR (low power DDR) DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. Unfortunately, there are two shortcomings of per-bank refresh employed in today's systems. First, we observe that the perbank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, as it is done today, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP proactively schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations on a wide variety of workloads and systems show that our mechanisms improve system performance (and energy efficiency) compared to three state-of-the-art refresh policies and the performance benefit increases as DRAM density increases.

Cache read misses stall the processor if there are no independent instructions to execute. In contrast, most cache write misses are off the critical path of execution, since writes can be buffered in the cache or the store buffer. With few exceptions, cache lines that serve loads are more critical for performance than cache lines that serve only stores. Unfortunately, traditional cache management mechanisms do not take into account this disparity between read-write criticality. This paper proposes a Read-Write Partitioning (RWP) policy that minimizes read misses by dynamically partitioning the cache into clean and dirty partitions, where partitions grow in size if they are more likely to receive future read requests. We show that exploiting the differences in read-write criticality provides better performance over prior cache management mechanisms. For a single-core system, RWP provides 5% average speedup across the entire SPEC CPU2006 suite, and 14% average speedup for cache-sensitive benchmarks, over the baseline LRU replacement policy. We also show that RWP can perform within 3% of a new yet complex instruction-address-based technique, Read Reference Predictor (RRP), that bypasses cache lines which are unlikely to receive any read requests, while requiring only 5.4% of RRP's state overhead. On a 4-core system, our RWP mechanism improves system throughput by 6% over the baseline and outperforms three other state-of-the-art mechanisms we evaluate.

Recent technology advancements allow for the integration of large memory structures on-die or as a die-stacked DRAM. Such structures provide higher bandwidth and faster access time than off-chip memory. Prior work has investigated using the large integrated memory as a cache, or using it as part of a heterogeneous memory system under management of the OS. Using this memory as a cache would waste a large fraction of total memory space, especially for the systems where stacked memory could be as large as off-chip memory. An OS managed heterogeneous memory system, on the other hand, requires costly usage-monitoring hardware to migrate frequently-used pages, and is often unable to capture pages that are highly utilized for short periods of time. This paper proposes a practical, low-cost architectural solution to efficiently enable using large fast memory as Part-of-Memory (PoM) seamlessly, without the involvement of the OS. Our PoM architecture effectively manages two different types of memory (slow and fast) combined to create a single physical address space. To achieve this, PoM implements the ability to dynamically remap regions of memory based on their access patterns and expected performance benefits. Our proposed PoM architecture improves performance by 18.4% over static mapping and by 10.5% over an ideal OS-based dynamic remapping policy.

As DRAM cells continue to shrink, they become more susceptible to retention failures. DRAM cells that permanently exhibit short retention times are fairly easy to identify and repair through the use of memory tests and row and column redundancy. However, the retention time of many cells may vary over time due to a property called Variable Retention Time (VRT). Since these cells intermittently transition between failing and non-failing states, they are particularly difficult to identify through memory tests alone. In addition, the high temperature packaging process may aggravate this problem as the susceptibility of cells to VRT increases after the assembly of DRAM chips. A promising alternative to manufacture-time testing is to detect and mitigate retention failures after the system has become operational. Such a system would require mechanisms to detect and mitigate retention failures in the field, but would be responsive to retention failures introduced after system assembly and could dramatically reduce the cost of testing, enabling much longer tests than are practical with manufacturer testing equipment.

Many enterprise and mobile systems must operate within strict power constraints. These systems dynamically trade off performance and power to maximize performance while keeping power within specified limits. In multi-core systems, maximizing the number of active cores within a strict power budget requires minimizing the power per core. Lowering core voltage dramatically reduces power, but compromises cache reliability. Mixed-cell cache architectures, where part of the cache is designed with larger, more robust cells, enable caches to operate reliably at low voltage while minimizing the added cost of larger cells. But mixed-cell caches suffer from poor low-voltage scalability since caches can only use robust cells at low voltage, sacrificing up to 75% of cache capacity. Such capacity reduction strains shared cache resources, leading to significant performance losses. In this paper, we propose a mixed-cell architecture that improves multi-core performance by allowing the use of both robust and non-robust cells. Our mechanisms store modified data only in robust lines by modifying the cache replacement policy and handling writes to non-robust lines. For a multi-core processor, our best mechanism improves performance by 17%, and reduces dynamic power in the L1 data cache by 50% over prior mixed-cell proposals.

Voltage scaling is one of the most effective mechanisms to improve microprocessors' energy efficiency. However, processors cannot operate reliably below a minimum voltage, Vccmin, since hardware structures may fail. Cell failures in large memory arrays (e.g., caches) typically determine Vccmin for the whole processor. We observe that most cache lines exhibit zero or one failures at low voltages. However, a few lines, especially in large caches, exhibit multi-bit failures and increase Vccmin. Previous solutions either significantly reduce cache capacity to enable uniform error correction across all lines, or significantly increase latency and bandwidth overheads when amortizing the cost of error-correcting codes (ECC) over large lines.

The performance/energy trade-off is widely acknowledged as a primary design consideration for modern processors. A less discussed, though equally important, trade-off is the reliability/energy trade-off. Many design features that increase reliability (e.g., redundancy, error detection, and correction) have the side effect of consuming more energy. Many energy-saving features (e.g., voltage scaling) have the side effect of making systems less reliable. In this paper, we propose an adaptive cache design that enables the operating system to optimize for performance or energy efficiency without sacrificing reliability. Our proposed mechanism enables a cache with a wide operating range, where the cache can use a variable part of its data array to store error-correcting codes. A reliable, energy-efficient cache can use up to half of its data array to store error-correcting codes so that it can reliably operate at a low voltage to reduce energy. A reliable high-performance cache uses its whole data array, but operates at a higher voltage to improve reliability while sacrificing energy. We propose a hardware mechanism that allows the operating system to choose different points within that operating range based on the desired levels of performance, energy, and reliability.

Technology advancements have enabled the integration of large on-die embedded DRAM (eDRAM) caches. eDRAM is significantly denser than traditional SRAMs, but must be periodically refreshed to retain data. Like SRAM, eDRAM is susceptible to device variations, which play a role in determining refresh time for eDRAM cells. Refresh power potentially represents a large fraction of overall system power, particularly during low-power states when the CPU is idle. Future designs need to reduce cache power without incurring the high cost of flushing cache data when entering low-power states. In this paper, we show the significant impact of variations on refresh time and cache power consumption for large eDRAM caches. We propose Hi-ECC, a technique that incorporates multi-bit error-correcting codes to significantly reduce refresh rate. Multi-bit error-correcting codes usually have a complex decoder design and high storage cost. Hi-ECC avoids the decoder complexity by using strong ECC codes to identify and disable sections of the cache with multi-bit failures, while providing efficient single-bit error correction for the common case. Hi-ECC includes additional optimizations that allow us to amortize the storage cost of the code over large data words, providing the benefit of multi-bit correction at same storage cost as a single-bit error-correcting (SECDED) code (2 % overhead). Our proposal achieves a 93 % reduction in refresh power vs. a baseline eDRAM cache without error correcting capability, and a 66 % reduction in refresh power vs. a system using SECDED codes.

Voltage scaling is one of the most effective mechanisms to reduce microprocessor power consumption. However, the increased severity of manufacturing-induced parameter variations at lower voltages limits voltage scaling to a minimum voltage, Vccmin, below which a processor cannot operate reliably. Memory cell failures in large memory structures (e.g., caches) typically determine the Vccmin for the whole processor. Memory failures can be persistent (i.e., failures at time zero which cause yield loss) or non-persistent (e.g., soft errors or erratic bit failures). Both types of failures increase as supply voltage decreases and both need to be addressed to achieve reliable operation at low voltages. In this paper, we propose a novel adaptive technique to improve cache lifetime reliability and enable low voltage operation. This technique, multi-bit segmented ECC (MS-ECC) addresses both persistent and non-persistent failures. Like previous work on mitigating persistent failures, MS-ECC trades off cache capacity for lower voltages. However, unlike previous schemes, MS-ECC does not rely on testing to identify and isolate defective bits, and therefore enables error tolerance for nonpersistent failures like erratic bits and soft errors at low voltages. Furthermore, MS-ECC’s design can allow the operating system to adaptively change the cache size and ECC capability to adjust to system operating conditions. Compared to current designs with single-bit correction, the most aggressive implementation for MS-ECC enables a 30 % reduction in supply voltage, reducing power by 71 % and energy per instruction by 42%.

A statistical performance simulator is developed to explore the impact of parameter variations on the maximum clock frequency (FMAX) and throughput distributions of multi-core processors in a future 22 nm technology. The simulator captures the effects of die-to-die (D2D) and within-die (WID) transistor and interconnect parameter variations on critical path delays in a die. A key component of the simulator is an analytical multi-core processor throughput model, which enables computationally efficient and accurate throughput calculations, as compared with cycle-accurate performance simulators, for single-threaded and highly parallel multi-threaded (MT) workloads. Based on microarchitecture designs from previous microprocessors, three multi-core processors with either small, medium, or large cores are projected for the 22 nm technology generation to investigate a range of design options. These three multi-core processors are optimized for maximum throughput within a constant die area. A traditional single-core processor is also scaled to the 22 nm technology to provide a baseline comparison.The salient contributions from this paper are: 1) product-level variation analysis for multi-core processors must focus on throughput, rather than just FMAX, and 2) multi-core processors are more variation tolerant than single-core processors due to the larger impact of memory latency and bandwidth on throughput.To elucidate these two points, statistical simulations indicate that multi-core and single-core processors with an equivalent total core area have similar FMAX distributions (mean degradation of 9% and standard deviation of 5%) for MT applications. In contrast to single-core processors, memory latency and bandwidth constraints significantly limit the throughput dependency on FMAX in multi-core processors, thus reducing the throughput mean degradation and standard deviation by ~50% for the small and medium core designs and by ~30% for the large core design. This improvement in the throughput distribution indicates that multi-core processors could significantly reduce the product design and process development complexities due to parameter variations as compared to single-core processors, enabling faster time to market for high-performance microprocessor products.

One of the most effective techniques to reduce a processor's power consumption is to reduce supply voltage. However, reducing voltage in the context of manufacturing-induced parameter variations can cause many types of memory circuits to fail. As a result, voltage scaling is limited by a minimum voltage, often called Vccmin, beyond which circuits may not operate reliably. Large memory structures (e.g., caches) typically set Vccmin for the whole processor. In this paper, we propose two architectural techniques that enable microprocessor caches (L1 and L2), to operate at low voltages despite very high memory cell failure rates. The Word-disable scheme combines two consecutive cache lines, to form a single cache line where only non-failing words are used. The Bit-fix scheme uses a quarter of the ways in a cache set to store positions and fix bits for failing bits in other ways of the set. During high voltage operation, both schemes allow use of the entire cache. During low voltage operation, they sacrifice cache capacity by 50% and 25%, respectively, to reduce Vccmin below 500mV. Compared to current designs with a Vccmin of 825 mV, our schemes enable a 40% voltage reduction, which reduces power by 85% and energy per instruction (EPI) by 53%.

In chip multiprocessors (CMPs), multiple cores compete for shared resources such as on-chip caches and off-chip pin bandwidth. Stride-based hardware prefetching increases demand for these resources, causing contention that can degrade performance (up to 35% for one of our benchmarks). In this paper, we first show that cache and link (off-chip interconnect) compression can increase the effective cache capacity (thereby reducing off-chip misses) and increase the effective off-chip bandwidth (reducing contention). On an 8-processor CMP with no prefetching, compression improves performance by up to 18% for commercial workloads. Second, we propose a simple adaptive prefetching mechanism that uses cache compressions extra tags to detect useless and harmful prefetches. Furthermore, in the central result of this paper, we show that compression and prefetching interact in a strong positive way, resulting in combined performance improvement of 10-51% for seven of our eight workloads

A statistical performance simulator is developed to explore the impact of die-to-die (D2D) and within-die (WID) parameter variations on the distributions of maximum clock frequency (FMAX) and throughput for multi-core processors in a future 22nm technology.allThe simulator integrates a compact analytical throughput model, which captures the key dependencies of multi-core processors, into a statistical simulation framework that models the effects of D2D and WID parameter variations on critical path delays across a die. The salient contributions from this paper are: (1) Product-level variation analysis for multi-core processors must focus on throughput, rather than just FMAX, and (2) Multi-core processors are inherently more variation tolerant than single-core processors due to the larger impact of memory latency and bandwidth on overall throughput. To elucidate these two points, multi-core and single-core processors have a similar chip-level FMAX distribution (mean degradation of 9% and standard deviation of 5%) for multi-threaded applications. In contrast to single-core processors, memory latency and bandwidth constraints significantly limit the throughput dependency on FMAX in multi-core processors, thus reducing the throughput mean degradation and standard deviation by 50%. Since single-threaded applications running on a multi-core processor can execute on the fastest core, mean FMAX and throughput gains of 4% are achieved from the nominal design target.

Modern processors use two or more levels of cache memories to bridge the rising disparity between processor and memory speeds. Compression can improve cache performance by increasing effective cache capacity and eliminating misses. However, decompressing cache lines also increases cache access latency, potentially degrading performance. In this paper, we develop an adaptive policy that dynamically adapts to the costs and benefits of cache compression. We propose a two-level cache hierarchy where the L1 cache holds uncompressed data and the L2 cache dynamically selects between compressed and uncompressed storage. The L2 cache is 8-way set-associative with LRU replacement, where each set can store up to eight compressed lines but has space for only four uncompressed lines. On each L2 reference, the LRU stack depth and compressed size determine whether compression (could have) eliminated a miss or incurs an unnecessary decompression overhead. Based on this outcome, the adaptive policy updates a single global saturating counter, which predicts whether to allocate lines in compressed or uncompressed form. We evaluate adaptive cache compression using full-system simulation and a range of benchmarks. We show that compression can improve performance for memory-intensive commercial workloads by up to 17%. However, always using compression hurts performance for low-miss-rate benchmarks - due to unnecessary decompression overhead - degrading performance by up to 18%. By dynamically monitoring workload behavior, the adaptive policy achieves comparable benefits from compression, while never degrading performance by more than 0.4%.

Multi-threaded commercial workloads implement many important Internet services. Consequently, these workloads are increasingly used to evaluate the performance of uniprocessor and multiprocessor system designs. This paper identifies performance variability as a potentially major challenge for architectural simulation studies using these workloads. Variability refers to the differences between multiple estimates of a workload's performance. Time variability occurs when a workload exhibits different characteristics during different phases of a single run. Space variability occurs when small variations in timing cause runs starting from the same initial condition to follow widely different execution paths. Variability is a well-known phenomenon in real systems, but is nearly universally ignored in simulation experiments. In a central result of this paper we show that variability in multi-threaded commercial workloads can lead to incorrect architectural conclusions (e.g., 31% of the time in one experiment). We propose a methodology, based on multiple simulations and standard statistical techniques, to compensate for variability. Our methodology greatly reduces the probability of reaching incorrect conclusions, while enabling simulations to finish within reasonable time limits.