A hybrid memory includes cache of relatively fast and durable dynamic, random-access memory (DRAM) in service of a larger amount of relatively slow and wear-sensitive flash memory. An address buffer on the module maintains a static, random-access memory (SRAM) cache of addresses for data cached in DRAM.

An approach is disclosed that configures a computer system node from components that are each connected to an intra-node network. The configuring is performed by selecting a set of components, including at least one processor, and assigning each of the components a different address range within the node. An operating system is run on the processor included in the node with the operating system accessing each of the assigned components.

Provided are a computer program product, system, and method for using a track format code in a cache control block for a track in a cache to process read and write requests to the track in the cache. A track format table associates track format codes with track format metadata. A determination is made as to whether the track format table has track format metadata matching track format metadata of a track staged into the cache. A determination is made as to whether a track format code from the track format table for the track format metadata in the track format table matches the track format metadata of the track staged. A cache control block for the track being added to the cache is generated including the determined track format code when the track format table has the matching track format metadata.

Provided are a computer program product, system, and method for using a track format code in a cache control block for a track in a cache to process read and write requests to the track in the cache. A track format table associates track format codes with track format metadata. A determination is made as to whether the track format table has track format metadata matching track format metadata of a track staged into the cache. A determination is made as to whether a track format code from the track format table for the track format metadata in the track format table matches the track format metadata of the track staged. A cache control block for the track being added to the cache is generated including the determined track format code when the track format table has the matching track format metadata.

A memory system of an embodiment includes a nonvolatile memory, a primary cache memory, a secondary cache memory, and a processor. The processor performs address conversion by using logical-to-physical address conversion information relating to data to be addressed in the nonvolatile memory. Based on whether first processing is performed on the nonvolatile memory or second processing is performed on the nonvolatile memory, the processor controls to store whether the logical-to-physical address conversion information relating to the first processing to be in the primary cache memory as cache data or logical-to-physical address conversion information relating to the second processing to be in the secondary cache memory as cache data.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

The present disclosure provides a computation device. The computation device is configured to perform a machine learning computation, and includes an operation unit, a controller unit, and a conversion unit. The storage unit is configured to obtain input data and a computation instruction. The controller unit is configured to extract and parse the computation instruction from the storage unit to obtain one or more operation instructions, and to send the one or more operation instructions and the input data to the operation unit. The operation unit is configured to perform operations on the input data according to one or more operation instructions to obtain a computation result of the computation instruction. In the examples of the present disclosure, the input data involved in machine learning computations is represented by fixed-point data, thereby improving the processing speed and efficiency of training operations.

A data access system including a processor, multiple cache modules for the main memory, and a storage drive. The cache modules include a FLC controller and a main memory cache. The multiple cache modules function as main memory. The processor sends read/write requests (with physical address) to the cache module. The cache module includes two or more stages with each stage including a FLC controller and DRAM (with associated controller). If the first stage FLC module does not include the physical address, the request is forwarded to a second stage FLC module. If the second stage FLC module does not include the physical address, the request is forwarded to the storage drive, a partition reserved for main memory. The first stage FLC module has high speed, lower power operation while the second stage FLC is a low-cost implementation. Multiple FLC modules may connect to the processor in parallel.

An apparatus including: a plurality of compute express link (CXL) devices each including a memory and a processor for processing works stored in the memory; and a switch configured to connect the CXL devices to each other, wherein a first CXL device among the plurality of CXL devices selects at least one second CXL device from at least some CXL devices of the plurality of CXL devices to distribute works stored in a memory of the first CXL device based on a usable capacity of a memory of the at least some CXL devices.

An apparatus including: a plurality of compute express link (CXL) devices each including a memory and a processor for processing works stored in the memory; and a switch configured to connect the CXL devices to each other, wherein a first CXL device among the plurality of CXL devices selects at least one second CXL device from at least some CXL devices of the plurality of CXL devices to distribute works stored in a memory of the first CXL device based on a usable capacity of a memory of the at least some CXL devices.