Quantitative Evaluation of Intel PEBS Overhead for Online System-Noise Analysis

Akiyama, Soramichi; Hirofuchi, Takahiro

doi:10.1145/3095770.3095773

Cited by 22 publications

(14 citation statements)

References 7 publications

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“…When monitoring in the PMI mode, the overhead mainly comes from PMI handling. When monitoring in the PEBS mode, although the interrupts are significantly reduced, there are still two sources of overhead: First, writing each PEBS record into the DS area takes about 200 ns [8]. Second, DS-area-overflow interrupts need to be generated when the DS area is full (maximum size is 4 MB).…”

Section: Trace Collectionmentioning

confidence: 99%

“…#PF, #GP, etc. ), we generate one attack variant for each of the following 23 value ranges for N a : {[1, 1], [2,2], [3,3], [4,4], [5,5], [6,6], [7,7], [8,8], [9,9], [10,10], [11,20], [21,30], [31,40], [41,50], [51,60] [10,001,100,000], [100,001, 1,000,000], [1,000,001, 2,000,000]}. In all attack variants, L a was chosen from 150K, 250K, 350K, 450K, 550K, 650K, 750K cycles.…”

Section: Exception-based Attack Variantsmentioning

confidence: 99%

“…), we generate one attack variant for each combination of N t , N a , L t , and L a . The values of N t and N a are sampled uniformly at random from the following 14 value ranges: [3,3], [4,4], [5,5], [6,6], [7,7], [8,8], [9,9], [10,10], [11,20], [21,30], [31,50], [51,100]}; The values of L t are chosen from {350, 450, 550, 650, 750} CPU cycles; and the values of L a are chosen from: {150K, 250K, 350K, 450K, 550K} CPU cycles. Therefore, in total 14 × 14 × 5 × 5 × 2 × 4 = 39, 200 attack variants were generated.…”

Section: Btb/pht Misprediction Variantsmentioning

confidence: 99%

See 2 more Smart Citations

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

Wang

Chen

Cheng³

et al. 2021

Detection of Intrusions and Malware, and Vulnerability Assessment

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This paper presents Specularizer, a framework for uncovering speculative execution attacks using performance tracing features available in commodity processors. It is motivated by the practical difficulty of eradicating such vulnerabilities in the design of CPU hardware and operating systems and the principle of defense-in-depth. The key idea of Specularizer is the use of Hardware Performance Counters and Processor Trace to perform lightweight monitoring of production applications and the use of machine learning techniques for identifying the occurrence of the attacks during offline forensics analysis. Different from prior works that use performance counters to detect side-channel attacks, Specularizer monitors triggers of the critical paths of the speculative execution attacks, thus making the detection mechanisms robust to different choices of side channels used in the attacks. To evaluate Specularizer, we model all known types of exception-based and misprediction-based speculative execution attacks and automatically generate thousands of attack variants. Experimental results show that Specularizer yields superior detection accuracy and the online tracing of Specularizer incur reasonable overhead.

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Section: Trace Collectionmentioning

confidence: 99%

Section: Exception-based Attack Variantsmentioning

confidence: 99%

Section: Btb/pht Misprediction Variantsmentioning

confidence: 99%

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Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

Wang

Chen

Cheng³

et al. 2021

Detection of Intrusions and Malware, and Vulnerability Assessment

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show abstract

“…Perfmon provides an interface for making easier to retrieve information from these hardware counters. The use of PEBS as a tool to monitor a program execution was already implemented in the work by Lorenzo et al, 21 which shows that they can provide information in runtime about the behavior of a code with low overhead 22 . They provided as well a straightforward thread migration tool tested with toy examples.…”

Section: Performance Profilingmentioning

confidence: 99%

LBMA and IMAR²: Weighted lottery based migration strategies for NUMA multiprocessing servers

Laso

Lorenzo

Rivera

et al. 2020

Concurrency and Computation

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Summary Multicore NUMA systems present on‐board memory hierarchies and communication networks that influence performance when executing shared memory parallel codes. Characterizing this influence is complex, and understanding the effect of particular hardware configurations on different codes is of paramount importance. In this article, monitoring information extracted from hardware counters at runtime is used to characterize the behavior of each thread for an arbitrary number of multithreaded processes running in a multiprocessing environment. This characterization is given in terms of number of operations per second, operational intensity, and latency of memory accesses. We propose a runtime tool, executed in user space, that uses this information to guide two different thread migration strategies for improving execution efficiency by increasing locality and affinity without requiring any modification in the running codes. Different configurations of NAS Parallel OpenMP benchmarks running concurrently on multicore NUMA systems were used to validate the benefits of our proposal, in which up to four processes are running simultaneously. In more than the 95% of the executions of our tool, results outperform those of the operating system (OS) and produces up to 38% improvement in execution time over the OS for heterogeneous workloads, under different and realistic locality and affinity scenarios.

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“…Also, the collection of a single sample is more efficient than instrumentation because it is done by the hardware. Collecting a sample with Intel PEBS only requires 200 to 300 nanoseconds [2]. The impact on the application performance is thus small.…”

Section: Collecting Memorymentioning

confidence: 99%

NumaMMA

Selva

Morel

Marquet

2018

Proceedings of the 47th International Conference on Parallel Processing

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Non Uniform Memory Access (NUMA) architectures are nowadays common for running High-Performance Computing (HPC) applications. In such architectures, several distinct physical memories are assembled to create a single shared memory. Nevertheless, because there are several physical memories, access times to these memories are not uniform depending on the location of the core performing the memory request and on the location of the target memory. Hence, threads and data placement are crucial to efficiently exploit such architectures. To help in taking decision about this placement, profiling tools are needed. In this work, we propose NUMA MeMory Analyzer (NumaMMA), a new profiling tool for understanding the memory access patterns of HPC applications. NumaMMA combines efficient collection of memory traces using hardware mechanisms with original visualization means allowing to see how memory access patterns evolve over time. The information reported by NumaMMA allows to understand the nature of these access patterns inside each object allocated by the application. We show how NumaMMA can help understanding the memory patterns of several HPC applications in order to optimize them and get speedups up to 28% over the standard non optimized version.

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Quantitative Evaluation of Intel PEBS Overhead for Online System-Noise Analysis

Cited by 22 publications

References 7 publications

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

LBMA and IMAR²: Weighted lottery based migration strategies for NUMA multiprocessing servers

NumaMMA

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Quantitative Evaluation of Intel PEBS Overhead for Online System-Noise Analysis

Cited by 22 publications

References 7 publications

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

Specularizer : Detecting Speculative Execution Attacks via Performance Tracing

LBMA and IMAR2: Weighted lottery based migration strategies for NUMA multiprocessing servers

NumaMMA

Contact Info

Product

Resources

About

LBMA and IMAR²: Weighted lottery based migration strategies for NUMA multiprocessing servers