Dynamic random-access memory (DRAM) stores each bit of data in a separate capacitor within an integrated circuit. It is structurally simple (only one transistor and a capacitor are required per bit) allowing DRAM to reach very high densities. The transistors and capacitors used are extremely small; billions can fit on a single memory chip. However, the memory is volatile and data is lost when power is removed.
There is a a research group at Stanford University who say the goal should be to replace hard disks with DRAM. RAMcloud is a general-purpose storage system where all data lives in DRAM at all times and large-scale systems are created by aggregating the main memories of thousands of commodity servers. RAMCloud provides durable and available DRAM-based storage for the same cost as volatile caches, and it offers performance 10-100x faster than existing storage systems. By combining low latency and large scale, RAMCloud will enable a new class of applications that manipulate large datasets more intensively than has ever been possible.
With increaed throughput (1m ops/sec/server) and low latency access the implication is that we will see a new class of applications that operate large scale using very large datasets. Possible applications are:
A useful introductory lecture by John Ousterhout is Professor (Research) of Computer Science at Stanford University can be watched here.
There is a a research group at Stanford University who say the goal should be to replace hard disks with DRAM. RAMcloud is a general-purpose storage system where all data lives in DRAM at all times and large-scale systems are created by aggregating the main memories of thousands of commodity servers. RAMCloud provides durable and available DRAM-based storage for the same cost as volatile caches, and it offers performance 10-100x faster than existing storage systems. By combining low latency and large scale, RAMCloud will enable a new class of applications that manipulate large datasets more intensively than has ever been possible.
With increaed throughput (1m ops/sec/server) and low latency access the implication is that we will see a new class of applications that operate large scale using very large datasets. Possible applications are:
- crowd level collaboration;
- large scale graph algorithms;
- real-time information intensive applications
- applications using open linked data
A useful introductory lecture by John Ousterhout is Professor (Research) of Computer Science at Stanford University can be watched here.
