High throughput heavy hitter aggregation for modern SIMD processors

Polychroniou, Orestis; Ross, Kenneth A.

doi:10.1145/2485278.2485284

Cited by 33 publications

(19 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This type of query has been successfully used in prior work to evaluate data aggregations [18]- [20]; its performance depends highly both on the underlying implementation as well as the characteristics of the input data. r is a two-column table with n rows consisting of a 32-bit integer group key g and a 32-bit integer value v. The result is a three-column output table where each row contains a group, the frequency of that group count and the sum of all values corresponding to that group sum.…”

Section: A Query and Input Datamentioning

confidence: 99%

See 1 more Smart Citation

Future vector microprocessor extensions for data aggregations

Hayes

Palomar

Ünsal

et al. 2016

SIGARCH Comput. Archit. News

View full text Add to dashboard Cite

Abstract-As the rate of annual data generation grows exponentially, there is a demand to aggregate and summarise vast amounts of information quickly. In the past, frequency scaling was relied upon to push application throughput. Today, Dennard scaling has ceased and further performance must come from exploiting parallelism. Single instruction-multiple data (SIMD) instruction sets offer a highly efficient and scalable way of exploiting data-level parallelism (DLP). While microprocessors originally offered very simple SIMD support targeted at multimedia applications, these extensions have been growing both in width and functionality. Observing this trend, we use a simulation framework to model future SIMD support and then propose and evaluate five different ways of vectorising data aggregation. We find that although data aggregation is abundant in DLP, it is often too irregular to be expressed efficiently using typical SIMD instructions. Based on this observation, we propose a set of novel algorithms and SIMD instructions to better capture this irregular DLP. Furthermore, we discover that the best algorithm is highly dependent on the characteristics of the input. Our proposed solution can dynamically choose the optimal algorithm in the majority of cases and achieves speedups between 2.7× and 7.6× over a scalar baseline.

show abstract

Section: A Query and Input Datamentioning

confidence: 99%

“…Polychroniou and Ross [20] propose SIMD optimisations when aggregating datasets similar to the zipf and hhitter datasets. Their approach uses multimedia SIMD extensions, although in a very different way to our vector SIMD instructions.…”

Section: A Parallel Aggregation Accelerationmentioning

confidence: 99%

Future vector microprocessor extensions for data aggregations

Hayes

Palomar

Ünsal

et al. 2016

SIGARCH Comput. Archit. News

View full text Add to dashboard Cite

show abstract

“…Zhou [16], range indexes [17], Bloom filters [18], hash tables and partitioning used in radixsort and hash joins [19].…”

Section: Related Workmentioning

confidence: 99%

Efficient Lightweight Compression Alongside Fast Scans

Polychroniou

Ross

2015

Proceedings of the 11th International Workshop on Data Management on New Hardware

Self Cite

View full text Add to dashboard Cite

The increasing main-memory capacity has allowed query execution to occur primarily in main memory. Database systems employ compression, not only to fit the data in main memory, but also to address the memory bandwidth bottleneck. Lightweight compression schemes focus on efficiency over compression rate and allow query operators to process the data in compressed form. For instance, dictionary compression keeps the distinct column values in a sorted dictionary and stores the values as index codes with the minimum number of bits. Packing the bits of each code contiguously, namely horizontal bit packing, has been optimized by using SIMD instructions for unpacking and by evaluating predicates in parallel per processor word for selection scans. Interleaving the bits of codes, namely vertical bit packing, provides faster scans, but incurs prohibitive costs for packing and unpacking. Here, we improve packing and unpacking for vertical bit packing using SIMD instructions, achieving more than an order of magnitude speedup. Also, we optimize horizontal bit packing on the latest CPUs and compare all approaches. While no single variant is better in all cases, vertical bit packing offers a good trade-off by combining the fastest scans with comparably fast packing and unpacking.

show abstract

“…Polychroniou et.al. [15] proposed updating the aggregates of heavy hitters using SIMD and in recent work [16] designed a SIMD-based range index to accelerate range partitioning and comparison sorting.…”

Section: Related Workmentioning

confidence: 99%

Vectorized Bloom filters for advanced SIMD processors

Polychroniou

Ross

2014

Proceedings of the Tenth International Workshop on Data Management on New Hardware

Self Cite

View full text Add to dashboard Cite

Analytics are at the core of many business intelligence tasks. Efficient query execution is facilitated by advanced hardware features, such as multi-core parallelism, shared-nothing low-latency caches, and SIMD vector instructions. Only recently, the SIMD capabilities of mainstream hardware have been augmented with wider vectors and non-contiguous loads termed gathers. While analytical DBMSs minimize the use of indexes in favor of scans based on sequential memory accesses, some data structures remain crucial. The Bloom filter, one such example, is the most efficient structure for filtering tuples based on their existence in a set and its performance is critical when joining tables with vastly different cardinalities. We introduce a vectorized implementation for probing Bloom filters based on gathers that eliminates conditional control flow and is independent of the SIMD length. Our techniques are generic and can be reused for accelerating other database operations. Our evaluation indicates a significant performance improvement over scalar code that can exceed 3X when the Bloom filter is cache-resident.

show abstract

High throughput heavy hitter aggregation for modern SIMD processors

Cited by 33 publications

References 21 publications

Future vector microprocessor extensions for data aggregations

Future vector microprocessor extensions for data aggregations

Efficient Lightweight Compression Alongside Fast Scans

Vectorized Bloom filters for advanced SIMD processors

Contact Info

Product

Resources

About