Breakthroughs in security, efficiency, and performance with on-die hetero-processing: How Hardware-Software co-design is redefining data management
Event details
| Date | 29.04.2016 |
| Hour | 12:00 › 13:00 |
| Speaker | Dr Garret Swart, Oracle |
| Location | |
| Category | Conferences - Seminars |
The Oracle database is the world's leading data management product. Soon after the acquisition of Sun Microsystems in 2010, engineers from Oracle and Sun started design of a new category of microprocessor that processes data several times faster, many times more efficiently, and qualitatively safer. This goal cannot be achieved by running software unchanged -- we needed to design new hardware and write new software to utilize it. The first step in this transformation was released as the SPARC M7 processor last year. The approach we took, and are taking, exploits the following ideas:
• Big is better: Large scale computing systems give access to huge amounts of data without the costs of moving data between systems. This allows for larger tables, bigger sorts, fatter graphs, and more cloud tenants sharing the the same resource pool on SPARC systems that scale linearly in cost and performance from 8 to 512 cores, 64 to 4096 threads.
• Secure is better: Cache-line level memory access checking allows our instrumented memory allocators to manage memory at production speed while detecting bugs and reporting attacks in real time.
• Information Density is better: With hardware designed for scanning n-gram compressed, bit packed, dictionary and run-length encoded columnar data at full memory bandwidth, we make maximal use of every bit stored and every cache line transferred over the memory channels with no impact on performance.
• Fast is better: With hardware support for running database operators on specialized streaming processors, we drive the memory channels at maximum rate, freeing up power and cores for running user computations on the result of these operators.
• Connected is better: Integrating EDR InfiniBand on-chip and on-board with low-latency, high-throughput, one-sided networking.
• Portable is better: By supporting platform independent acceleration APIs inside the database we support a wide variety of acceleration techniques and give applications and query planners the information to make the best use of the available hardware.
• Integrated is better: By supporting and accelerating multiple storage types (In-memory, NVMe, Exadata, NFS, HDFS, Fibre Channel), data formats (row major, column major, graph, JSON, spatial, MIME, Hive), algorithms, query languages, network protocols, and hardware platforms in a single product, we optimize, share resources, increase usability and reduce the cost and the cognitive load in acquiring, storing, securing and understanding data.
In this talk, I will describe the experience that drives our acceleration priorities, the constraints and joys of the hardware-software co-design process, the HW features that resulted, and how software engineers have exploiting these features in ways we expected and ways we didn't. The industry has taken this approach used in the acceleration of linear algebra and computer graphics, and in this talk we'll see how we apply it to data processing but with changes to match the lower compute density of the problem space.
• Big is better: Large scale computing systems give access to huge amounts of data without the costs of moving data between systems. This allows for larger tables, bigger sorts, fatter graphs, and more cloud tenants sharing the the same resource pool on SPARC systems that scale linearly in cost and performance from 8 to 512 cores, 64 to 4096 threads.
• Secure is better: Cache-line level memory access checking allows our instrumented memory allocators to manage memory at production speed while detecting bugs and reporting attacks in real time.
• Information Density is better: With hardware designed for scanning n-gram compressed, bit packed, dictionary and run-length encoded columnar data at full memory bandwidth, we make maximal use of every bit stored and every cache line transferred over the memory channels with no impact on performance.
• Fast is better: With hardware support for running database operators on specialized streaming processors, we drive the memory channels at maximum rate, freeing up power and cores for running user computations on the result of these operators.
• Connected is better: Integrating EDR InfiniBand on-chip and on-board with low-latency, high-throughput, one-sided networking.
• Portable is better: By supporting platform independent acceleration APIs inside the database we support a wide variety of acceleration techniques and give applications and query planners the information to make the best use of the available hardware.
• Integrated is better: By supporting and accelerating multiple storage types (In-memory, NVMe, Exadata, NFS, HDFS, Fibre Channel), data formats (row major, column major, graph, JSON, spatial, MIME, Hive), algorithms, query languages, network protocols, and hardware platforms in a single product, we optimize, share resources, increase usability and reduce the cost and the cognitive load in acquiring, storing, securing and understanding data.
In this talk, I will describe the experience that drives our acceleration priorities, the constraints and joys of the hardware-software co-design process, the HW features that resulted, and how software engineers have exploiting these features in ways we expected and ways we didn't. The industry has taken this approach used in the acceleration of linear algebra and computer graphics, and in this talk we'll see how we apply it to data processing but with changes to match the lower compute density of the problem space.
Practical information
- General public
- Free
Organizer
- Prof. Anastasia Ailamaki
Contact
- Dimitra Tsaoussis-Melissargps