gFPC: A Self-Tuning Compression Algorithm

Burtscher, Martin; Ratanaworabhan, Paruj

doi:10.1109/dcc.2010.42

Cited by 10 publications

(7 citation statements)

References 13 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The memory footprint of these benchmarks has many uncompressible blocks. Note that workloads with low compressibility might still benefit from special-purpose compression algorithms (Ratanaworabhan et al 2006;Burtscher et al 2007Burtscher et al , 2010.…”

Section: Which Application Domain To Compress?mentioning

confidence: 99%

Could Compression Be of General Use? Evaluating Memory Compression across Domains

Sardashti

Wood

2017

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

Recent proposals present compression as a cost-effective technique to increase cache and memory capacity and bandwidth. While these proposals show potentials of compression, there are several open questions to adopt these proposals in real systems including the following: (1) Do these techniques work for real-world workloads running for long time? (2) Which application domains would potentially benefit the most from compression? (3) At which level of memory hierarchy should we apply compression: caches, main memory, or both? In this article, our goal is to shed light on some main questions on applicability of compression. We evaluate compression in the memory hierarchy for selected examples from different application classes. We analyze real applications with real data and complete runs of several benchmarks. While simulators provide a pretty accurate framework to study potential performance/energy impacts of ideas, they mostly limit us to a small range of workloads with short runtimes. To enable studying real workloads, we introduce a fast and simple methodology to get samples of memory and cache contents of a real machine (a desktop or a server). Compared to a cycle-accurate simulator, our methodology allows us to study real workloads as well as benchmarks. Our toolset is not a replacement for simulators but mostly complements them. While we can use a simulator to measure performance/energy impact of a particular compression proposal, here with our methodology we can study the potentials with long running workloads in early stages of the design. Using our toolset, we evaluate a collection of workloads from different domains, such as a web server of CS department of UW-Madison for 24h, Google Chrome (watching a 1h-long movie on YouTube), and Linux games (playing for about an hour). We also use several benchmarks from different domains, including SPEC, mobile, and big data. We run these benchmarks to completion. Using these workloads and our toolset, we analyze different compression properties for both real applications and benchmarks. We focus on eight main hypotheses on compression, derived from previous work on compression. These properties (Table 2) act as foundation of several proposals on compression, so performance of those proposals depends very much on these basic properties. Overall, our results suggest that compression could be of general use both in main memory and caches. On average, the compression ratio is ≥2 for 64% and 54% of workloads, respectively, for memory and cache data. Our evaluation indicates significant potential for both cache and memory compression, with higher compressibility in memory due to abundance of zero blocks. Among application domains we studied, servers show on average the highest compressibility, while our mobile benchmarks show the lowest compressibility. For comparing benchmarks with real workloads, we show that (1) it is critical to run benchmarks to completion or considerably long runtimes to avoid biased conclusions, and (2) SPEC benchmarks are good representative of ...

show abstract

Section: Which Application Domain To Compress?mentioning

confidence: 99%

Could Compression Be of General Use? Evaluating Memory Compression across Domains

Sardashti

Wood

2017

ACM Trans. Archit. Code Optim.

View full text Add to dashboard Cite

show abstract

“…In the work of You and Sung, a regression line computed from the last compressed values is used to predict the next value. The works of Ratanaworabhan et al and Burtscher and Ratanaworabhan() use a forecast system based on jump address predictors for CPUs.…”

Section: Related Workmentioning

confidence: 99%

Scalable processing and autocovariance computation of big functional data

et al. 2017

View full text Add to dashboard Cite

SummaryThis paper presents 2 main contributions. The first is a compact representation of huge sets of functional data or trajectories of continuous-time stochastic processes, which allows keeping the data always compressed even during the processing in main memory. It is oriented to facilitate the efficient computation of the sample autocovariance function without a previous decompression of the data set, by using only partial local decoding. The second contribution is a new memory-efficient algorithm to compute the sample autocovariance function.The combination of the compact representation and the new memory-efficient algorithm obtained in our experiments the following benefits. The compressed data occupy in the disk 75% of the space needed by the original data. The computation of the autocovariance function used up to 13 times less main memory, and run 65% faster than the classical method implemented, for example, in the R package.

show abstract

“…Predictive coding schemes based on polynomial extrapolation or interpolation have been applied in a range of areas, including mesh compression [44,42,26], volume compres-B735 sion [23,17], audio compression [36,11], image compression [45,37,27], and floating point compression [30,14]. Predictive coding based on statistical techniques was used in [2], while hash functions were used in [35,9,10]. Alternative approaches attempt to preprocess datasets so as to make them more amenable to compression [28,40,38].…”

Section: Introduction the Advent Of Exascale Systemsmentioning

confidence: 99%

“…We develop a few methods of doing this in sections 5 and 6. Figure 7 illustrates the relationship between the value for R obtained using (9), the bound on R obtained using lower bound (10), and the actual compression ratio achieved by Algorithm 3. Figure 8 shows that the linear predictor is responsible for the bulk of the achieved compression.…”

mentioning

confidence: 99%

Compression Using Lossless Decimation: Analysis and Application

Ainsworth¹,

Klasky²,

Whitney³

2017

SIAM J. Sci. Comput.

View full text Add to dashboard Cite

A crude but commonly used technique for compressing ordered scientific data consists of simply retaining every sth datum (with a value of s = 10 generally the default) and discarding the remainder. Should the value of a discarded datum be required afterwards, an approximation is generated by linear interpolation of the two nearest retained values. Despite the widespread use of this and similar techniques, there is little by way of theoretical analysis of their expected performance. First, we quantify the accuracy achieved by linear interpolation when approximating values discarded by decimation, obtaining both deterministic bounds in terms of appropriate smoothness measures of the data and probabilistic bounds in terms of statistics of the data. Second, we investigate the efficiency of the lossless compression scheme consisting of decimation coupled with encoding of the interpolation errors. In particular, we bound the expected compression ratio in terms of the appropriate measures of the data. Finally, we provide numerical illustrations of the practical performance of the algorithm on some real datasets.

show abstract

gFPC: A Self-Tuning Compression Algorithm

Abstract: Abstract

Cited by 10 publications

References 13 publications

Could Compression Be of General Use? Evaluating Memory Compression across Domains

Could Compression Be of General Use? Evaluating Memory Compression across Domains

Scalable processing and autocovariance computation of big functional data

Compression Using Lossless Decimation: Analysis and Application

Contact Info

Product

Resources

About