Serverless computing has a critical problem of function cold starts. To minimize cold starts, state-of-the-art techniques predict function invocation times to warm them up. Warmed-up functions occupy space in memory and incur a keep-alive cost, which can become exceedingly prohibitive under bursty load. To address this issue, we design CodeCrunch, which introduces the concept of serverless function compression and exploits server heterogeneity to make serverless computing more efficient, especially under high memory pressure.

Transparently checkpointing MPI for fault tolerance and load balancing is a long-standing problem in HPC. The problem has been complicated by the need to provide checkpoint-restart services for all combinations of an MPI implementation over all network interconnects. This work presents MANA (MPI-Agnostic Network-Agnostic transparent checkpointing), a single code base which supports all MPI implementation and interconnect combinations. The agnostic properties imply that one can checkpoint an MPI application under one MPI implementation and perhaps over TCP, and then restart under a second MPI implementation over InfiniBand on a cluster with a different number of CPU cores per node. This technique is based on a novel "split-process" approach, which enables two separate programs to co-exist within a single process with a single address space. This work overcomes the limitations of the two most widely adopted transparent checkpointing solutions, BLCR and DMTCP/InfiniBand, which require separate modifications to each MPI implementation and/or underlying network API. The runtime overhead is found to be insignificant both for checkpoint-restart within a single host, and when comparing a local MPI computation that was migrated to a remote cluster against an ordinary MPI computation running natively on that same remote cluster.

Unified Virtual Memory (UVM) was recently introduced with CUDA version 8 and the Pascal GPU. The older CUDA programming style is akin to older large-memory UNIX applications which used to directly load and unload memory segments. Newer CUDA programs have started taking advantage of UVM for the same reasons of superior programmability that UNIX applications long ago switched to assuming the presence of virtual memory. Therefore, checkpointing of UVM has become increasing important, especially as NVIDIA CUDA continues to gain wider popularity: 87 of the top 500 supercomputers in the latest listings use NVIDIA GPUs, with a current trend of ten additional NVIDIA-based supercomputers each year. A new scalable checkpointing mechanism, CRUM (Checkpoint-Restart for Unified Memory), is demonstrated for hybrid CUDA/MPI computations across multiple computer nodes. The support for UVM is particularly attractive for programs requiring more memory than resides on the GPU, since the alternative to UVM is for the application to directly copy memory between device and host. Furthermore, CRUM supports a fast, forked checkpointing, which mostly overlaps the CUDA computation with storage of the checkpoint image in stable storage. The runtime overhead of using CRUM is 6% on average, and the time for forked checkpointing is seen to be a factor of up to 40 times less than traditional, synchronous checkpointing.

Large-scale applications rely on resilience mechanisms such as checkpoint-restart to make forward progress in the presence of failures. Unfortunately, this incurs huge I/O overhead and impedes productivity. To mitigate this challenge, this paper introduces a new technique, Shiraz, which demonstrates how to exploit differences in the checkpointing overhead among applications and knowledge of temporal characteristics of failures to improve both the overall system throughput and performance of individual applications.

System-level checkpoint-restart is a critical technology for long-running jobs in high-performance computing. Yet, only two approaches to checkpointing MPI applications continue to survive in wide use today. One approach is to use the kernel module-based BLCR in combination with an MPI checkpoint-restart service particular to the MPI implementation in use. Unfortunately, this lacks support for some important Linux system services such as SysV IPC (e.g., shared memory objects). A second approach has been to use the original 2009 DMTCP implementation (herein referred to as DMTCP-09) for transparent, system-level checkpointing. Unfortunately, DMTCP-09 lacked support for checkpointing many of the necessary features found by MPI in a modern batch environment. These include: ssh, the InfiniBand network, process migration (restarting an MPI application on different cluster nodes), and modified file path prefixes on restart (typically due to a changing current directory, mount points, library paths, etc.). This work presents DMTCP-PV, a new user-space transparent checkpointing system based on the concept of process virtualization. This approach separately models the state of each local or distributed subsystem while decoupling it from the core checkpointing engine. By separating these concerns, a domain expert can extend checkpointing into a new domain without any knowledge of the core checkpointing engine. This allowed DMTCP-PV to address the deficiencies noted above and many others. It is shown that the runtime overhead of DMTCP-PV is generally less than 1%, and the checkpointing time is dominated by the time to write an image file to stable storage.

Fault tolerance for the upcoming exascale generation has long been an area of active research. One of the components of a fault tolerance strategy is checkpointing. Petascale-level checkpointing is demonstrated through a new mechanism for virtualization of the InfiniBand UD (unreliable datagram) mode, and for updating the remote address on each UD-based send, due to lack of a fixed peer. Note that InfiniBand UD is required to support modern MPI implementations. An extrapolation from the current results to future SSD-based storage systems provides evidence that the current approach will remain practical in the exascale generation. This transparent checkpointing approach is evaluated using a framework of the DMTCP checkpointing package. Results are shown for HPCG (linear algebra), NAMD (molecular dynamics), and the NAS NPB benchmarks. In tests up to 32,752 MPI processes on 32,752 CPU cores, checkpointing of a computation with a 38 TB memory footprint in 11 minutes is demonstrated. Runtime overhead is reduced to less than 1%. The approach is also evaluated across three widely used MPI implementations.

The ability to easily deploy parallel computations on the Cloud is becoming ever more important. The first uniform mechanism for checkpointing a network of virtual machines is described. This is important for the parallel versions of common productivity software. Potential examples of parallelism include Simulink for MATLAB, parallel R for the R statistical modelling language, parallel blast.py for the BLAST bioinformatics software, IPython.parallel for Python, and GNU parallel for parallel shells. The checkpoint mechanism is implemented as a plugin in the DMTCP checkpoint-restart package. It operates on KVM/QEMU, and has also been adapted to Lguest and pure user-space QEMU. The plugin is surprisingly compact, comprising just 400 lines of code to checkpoint a single virtual machine, and 200 lines of code for a plugin to support saving and restoring network state. Incremental checkpoints of the associated virtual filesystem are accommodated through the Btrfs filesystem. Experiments demonstrate checkpoint times of a fraction of a second by using forked checkpointing, mmap-based restart, and incremental Btrfs-based snapshots.

In cluster computing, power and cooling represent a significant cost compared to the hardware itself. This is of special concern in the cloud, which provides access to large numbers of computers. We examine the use of ARM-based clusters for low-power, high performance computing. This work examines two likely use-modes: (i) a standard dedicated cluster, and (ii) a cluster of pre-configured virtual machines in the cloud. A 40-node department-level cluster based on an ARM Cortex-A9 is compared against a similar cluster based on an Intel Core2 Duo, in contrast to a recent similar study on just a 4-node cluster. For the NAS benchmarks on 32-node clusters, ARM was found to have a power efficiency ranging from 1.3 to 6.2 times greater than that of Intel. This is despite Intel's approximately five times greater performance. The particular efficiency ratio depends primarily on the size of the working set relative to L2 cache. In addition to energy-efficient computing, this study also emphasizes fault tolerance: an important ingredient in high performance computing. It relies on two recent extensions to the DMTCP checkpoint-restart package. DMTCP was extended (i) to support ARM CPUs, and (ii) to support check pointing of the Qemu virtual machine in user-mode. DMTCP is used both to checkpoint native distributed applications, and to checkpoint a network of virtual machines. This latter case demonstrates the ability to deploy pre-configured software in virtual machines hosted in the cloud, and further to migrate cluster computation between hosts in the cloud.