Use Case
Slashing cloud infrastructure costs through optimization
Optimize your cloud in real time
- Use AI to automatically adapt your infrastructure to the current load
- Ensure maximum performance at minimal cost
- Realize the full potential of your infrastructure
Right-size resources
- Pay only for the resources you actually need
- Speed up customer-facing workloads as well as batch jobs
- Reduce CPU utilization, lower resource consumption
Use Case
Cloud migration - optimizing for speed and cost after "lift and shift", without modifying the applications
Adapt the cloud infrastructure for your legacy applications
- Costly application redesigns are avoided by adapting the cloud infrastructure to your legacy applications that were not originally designed for the cloud
- Cloud migration happens quicker and at lower on-going costs
- Dynamic autonomous tuning continues to increase performance over time ensuring you always have the correct settings, even for new versions of your applications
Use Case
Boosting efficiency and reducing CapEx of on-premises infrastructure
Boost efficiency
- Pay only for the resources you actually need
- Speed up customer-facing workloads as well as batch jobs
- Reduce CPU utilization and lower resource consumption
- Minimize support tickets related to performance
Reduce CapEx
- Extend the life of existing systems to reduce CapEx
- Speed up customer-facing workloads as well as batch jobs
- Free up resources for immediate capacity gains
- Drive smarter purchasing decisions through benchmarking
Win with the most advanced optimization technology
- Advanced evolution and greedy optimization algorithms for the highest performance
- Deal with variability in a scientific way, with thresholds, timeouts, resampling, point estimators and statistical T-tests
- Integrations with benchmarking tools such as SPEC-CPU, MLPerf and others
- Configuration refinement algorithm for publishing minimal configurations
Use Case
Optimize Kubernetes ClustersBoosting benchmark results
Right-size k8s resources
- Automatically discover the optimal resources to allocate per pod
- Significantly improve efficiency and cut costs of your cluster
Dynamically tune k8s nodes
- Deploy dynamic tuning on k8s nodes to automatically tailor the OS settings to the needs of the pods on the node
- Automatically accelerate pods and optimize their use of system calls
Use Case
Compiler Flag Mining
Boost performance by choosing the optimal compilation flags
- The most advanced optimization technology for huge parameter spaces of compiler flag mining experiments
- Advanced algorithm to identify and avoid conflicting compiler flag combinations
- Support for GCC, LLVM, ICC, and AOCC compilers
Use Case
Boost the performance of legacy applications
Boost runtime performance
- Tune your garbage collector and heap parameters for Java, .net, Python, and Go applications
- Align your runtime resources with the actual infrastructure resources
- Leverage 100's of out-of-the-box tunables for popular runtimes
Accelerate C/C++ applications
- Mine compiler flags for best performance of your compiled applications
- Use accelerators to improve performance without changing your code or build process
- Accelerate writes to log files
Use Case
Optimization of CPU products
Higher CPU product performance without redesigning the hardware
- Boost CPU product performance by leveraging the many configuration and chicken bits embedded in the silicon and firmware
- Automate the post-silicon performance optimization phase to avoid human errors in the tuning process and shorten the overall time-to-market
- Tailor products for more customers while spending fewer efforts on tuning for each individual customer
Design-space exploration
- Connect your synthesis and simulation tools for design-space exploration
- Accurately tailor design parameters such as instruction cache size Vs. branch predictor size
- Integrate design space exploration in nightly regression tests
Boost profits by reducing tick-to-trade latencies
- Tune all of the components involved in the tick-to-trade journey: the NIC, the NIC driver, the BIOS, the processor, the firmware, the operating system, and the trading application
- Focus on perfecting your algorithms and architecture, leaving the grunt work of parameter tuning to an automated tool
- Tune model and application hyperparameters in order to improve their accuracy and shorten release cycles
- Optimize which symbol will be traded and on which device
