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Intel® Performance Counter Monitor (Intel® PCM) is an application programming interface (API) and a set of tools based on the API to monitor performance and energy metrics of Intel® Core™, Xeon®, Atom™ and Xeon Phi™ processors. PCM works on Linux, Windows, Mac OS X, FreeBSD, DragonFlyBSD and ChromeOS operating systems.

command line based tool to collect Perfmon Counting data from Hardware PMU C ounters on Intel x86 processors Supported Platforms <> Server - Intel 5th Gen Xeon Processor (Emerald Rapids) -> Core (Raptor Cove) - Intel 4th Gen Xeon Processor (Sapphire Rapids) -> Core (Golden Cove) -> CHA <> Client - Intel 14th Gen Client Processor (Meteorlake) -> P-CORE (Redwood Cove) -> E-CORE (Crestmont) - Intel 13th Gen Client Processor (Raptorlake) -> P-CORE (Raptor Cove) -> E-CORE (Gracemont) -> ARB - Intel 12th Gen Client Processor (Alderlake) -> P-CORE (Golden Cove) -> E-CORE (Gracemont) -> ARB

...If assembly is too much of a hassle but you still want access to specific machine instructions, consider Intrinsics-Dude. The instruction sets of the x86 and the x64, but also SSE, AVX, AVX2, Xeon-Phi (Knights Corner) instructions with their descriptions are provided. Most of the regularly used Masm directives are supported and some Nasm directives. If you are not happy with highlighting or the descriptions. Mnemonics and descriptions can be added and changed by updating the AsmDudeData.xml file that will be stored next to the binaries when installing the plugin (.vsix).

...The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. The Intel Math Kernel Library takes advantages of the parallelization and vectorization capabilities of Intel Xeon and Xeon Phi systems. When hyperthreading is enabled on the system, we recommend the following KMP_AFFINITY setting to make sure parallel threads are 1:1 mapped to the available physical cores.

The first parallel Smith-Waterman algorithm exploiting Intel Xeon Phi clusters to accelerate the alignment of long DNA sequences. This algorithm is written in C++ (with a set of SIMD intrinsic extensions), OpenMP and MPI. The performance evaluation revealed that our algorithm achieves very stable performance, and yields a performance of up to 30.1 GCUPS on a single Xeon Phi and up to 111.4 GCUPS on four Xeon Phis sharing a host.