A program's cache performance can be improved by changing the organization and layout of its data-even complex, pointer-based data structures. Previous techniques improved the cache performance of these structures by arranging distinct instances to increase reference locality. These techniques produced significant performance improvements, but worked best for small structures that could be packed into a cache block. This paper extends that work by concentrating on the internal organization of fields in a data structure. It describes two techniquesstructure splitting and field reordering--that improve the cache behavior of structures larger than a cache block. For structures comparable in size to a cache block, structure splitting can increase the number of hot fields that can be placed in a cache block. In five Java programs, structure splitting reduced cache miss rates 1 O-27% and improved performance 648% beyond the benefits of previously described cache-conscious reorganization techniques.For large structures, which span many cache blocks, reordering fields, to place those with high temporal affinity in the same cache block can also improve cache utilization. This paper describes bbcache, a tool that recommends C structure field reorderings. Preliminary measurements indicate that reordering fields in 5 active structures improves the performance of Microsoft SQL Server 7.0 2-3%.

Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community—enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes. Significance: To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community. This article is highlighted in the In This Issue feature, p. 995

Background Sugarcane cultivars are polyploid interspecific hybrids of giant genomes, typically with 10–13 sets of chromosomes from 2 Saccharum species. The ploidy, hybridity, and size of the genome, estimated to have >10 Gb, pose a challenge for sequencing. Results Here we present a gene space assembly of SP80-3280, including 373,869 putative genes and their potential regulatory regions. The alignment of single-copy genes in diploid grasses to the putative genes indicates that we could resolve 2–6 (up to 15) putative homo(eo)logs that are 99.1% identical within their coding sequences. Dissimilarities increase in their regulatory regions, and gene promoter analysis shows differences in regulatory elements within gene families that are expressed in a species-specific manner. We exemplify these differences for sucrose synthase (SuSy) and phenylalanine ammonia-lyase (PAL), 2 gene families central to carbon partitioning. SP80-3280 has particular regulatory elements involved in sucrose synthesis not found in the ancestor Saccharum spontaneum. PAL regulatory elements are found in co-expressed genes related to fiber synthesis within gene networks defined during plant growth and maturation. Comparison with sorghum reveals predominantly bi-allelic variations in sugarcane, consistent with the formation of 2 “subgenomes” after their divergence ∼3.8–4.6 million years ago and reveals single-nucleotide variants that may underlie their differences. Conclusions This assembly represents a large step towards a whole-genome assembly of a commercial sugarcane cultivar. It includes a rich diversity of genes and homo(eo)logous resolution for a representative fraction of the gene space, relevant to improve biomass and food production.

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud based data sharing ecosystem developed via collaboration between St. Jude Children's Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Jude's prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.

A program's cache performance can be improved by changing the organization and layout of its data-even complex, pointer-based data structures. Previous techniques improved the cache performance of these structures by arranging distinct instances to increase reference locality. These techniques produced significant performance improvements, but worked best for small structures that could be packed into a cache block. This paper extends that work by concentrating on the internal organization of fields in a data structure. It describes two techniquesstructure splitting and field reordering--that improve the cache behavior of structures larger than a cache block. For structures comparable in size to a cache block, structure splitting can increase the number of hot fields that can be placed in a cache block. In five Java programs, structure splitting reduced cache miss rates 1 O-27% and improved performance 648% beyond the benefits of previously described cache-conscious reorganization techniques.For large structures, which span many cache blocks, reordering fields, to place those with high temporal affinity in the same cache block can also improve cache utilization. This paper describes bbcache, a tool that recommends C structure field reorderings. Preliminary measurements indicate that reordering fields in 5 active structures improves the performance of Microsoft SQL Server 7.0 2-3%.