Search Results for "avr-llvm"

CodeChecker is a static analysis infrastructure built on the LLVM/Clang Static Analyzer toolchain, replacing scan-build in a Linux or macOS (OS X) development environment. Executes Clang-Tidy and Clang Static Analyzer with Cross-Translation Unit analysis, Statistical Analysis (when checkers are available). Creates the JSON compilation database by wiretapping any build process (e.g., CodeChecker log -b "make").

Binding is statically linked with a specific version of RocksDB. If you want to build it yourself, make sure you've also cloned the RocksDB and compression submodules. By default, support for the Snappy, LZ4, Zstd, Zlib, and Bzip2 compression is enabled through crate features. If support for all of these compression algorithms is not needed, default features can be disabled and specific compression algorithms can be enabled. The underlying RocksDB does allow column families to be created and...

Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for the right data. Utilize the same file and data formats and metadata, security, and...

NoisePage is a relational database management system (DBMS) designed from the ground up for autonomous deployment. It uses integrated machine learning components to control its configuration, optimization, and tuning. The system will support automated physical database design (e.g., indexes, materialized views, sharding), knob configuration tuning, SQL tuning, and hardware capacity/scaling. Our research focuses on building the system components that support such self-driving operations with...

...This 3rd version (started in 2006) helps to implement, preserve, share and reproduce the whole experimental setup as connected modules and data. cM uses crowdsourcing to leverage knowledge and computational resources of multiple users. For example, it includes multi-objective GCC, LLVM and ICC auto-tuning scenarios using shared benchmarks, codelets, data sets, tools, and combined with classification and predictive models. cM includes OpenME interactive interface to open up and expose internals of various third-party tools such as GCC, LLVM, run-time systems, etc. and connect them to cM through dynamic plugins that allows online analysis and tuning of programs and architectures. ...