Pathfinder Powers a New Generation of Graph Analytics
Pathfinder can power open source, commercial, or custom-developed graph databases using one of three approaches:
- In the native programming architecture, calls to the optimized Lucata algorithm library run natively, including Breadth-First Search (BFS), PageRank, BLAST, Connected Component, Scored Search, Triangle Count, and K-truss Subgraph
- API calls for GraphBLAS queries supported on the Lucata platform, such as Sparse Matrix Multiply. The open source LAGRAPH library provides an extensive collection of functions constructed with this hybrid programming architecture.
- With the RedisGraph programming architecture, you run RedisGraph queries on Lucata using the optimized data loader and RedisGraph application code which have been ported to Lucata and optimized for use with Lucata Migratory Thread technology
Access the Lucata SDK (Software Development Kit) for more details.
Pathfinder can easily scale to meet your needs with no manual reprogramming required; it runs BFS analytics on petabyte-scale graph databases. A Lucata Pathfinder-S chassis features 512 GB of RAM and a minimum of 8 TB of SSD, which allows you to run real-time analytics on a 512 GB graph database with no database sharding while storing up to 8 TBs of graph data in near-memory. Each Pathfinder chassis can support up to eight 100 GBit I/O cards, allowing your graph database engine to ingest data at its maximum rate. A Pathfinder rack seamlessly connects eight Pathfinder chassis, providing 4 TB of RAM and a minimum of 64 TB of SSD. Over 1,000 racks can be interconnected as a single shared memory image, supporting over 4 PB of RAM and 64 PB of SSD.
There is no data pruning required for graph analytics or machine or deep learning training, eliminating the potential for introducing bias during pruning or sharding processes.
Pathfinder is a Smart Choice
The Pathfinder platform is cost-effective. The patented Lucata migratory thread approach to computing means compute threads move to your data rather than moving data to the CPUs. This approach results in much lower interconnect bandwidth requirements between Pathfinder chassis and racks than current approaches to distributed computing clusters require. Migratory thread computing allows Pathfinder chassis to consistently maintain high CPU and RAM utilization rates, lowering your operating costs.
Pathfinder is a new computing paradigm for big data. You can process petabyte-scale datasets in real-time with no data pruning or database sharding. Initially available for graph databases, the Pathfinder platform has the capability to power massive performance gains for a variety of other database types. Pathfinder can also power high-performance machine and AI model training on sparse data.
Scalable Shared Memory
Unprecedented performance and scalable to 4 PB of RAM for in-memory databases and data lake acceleration
Removes coherency cost and complexity overheads in shared memory databases for OLTP
Ultra lightweight computational elements for MPP databases combined with Migratory Threads for OLAP enable efficient queries without data movement
Low Power Consumption
60%+ reduction in power consumption for environmentally conscious data science